National Journal of Speech and Audio Processing
National Journal of Speech and Audio Processing, ISSN 3107-5258, is dedicated to the advancement of knowledge in the areas of speech, audio, and acoustic signal processing. It welcomes original contributions that explore theoretical foundations, algorithmic innovations, and practical applications in speech and audio technologies. Scope Includes (but is not limited to): Speech recognition and synthesis systems Audio signal enhancement and noise reduction Voice biometrics and speaker identification Natural language processing (NLP) and conversational AI Acoustic modeling and environmental sound classification Music signal analysis and processing Real-time speech/audio systems for mobile and embedded platforms Multimodal interfaces (audio-visual systems) Deep learning in speech/audio analytics Applications in hearing aids, smart assistants, and robotics Frequency of publication - Quarterly Language - English Subject - Engineering Year of Starting - 2025 format of publication - Online Only ISSN - 3107-5258
SOCIETY FOR COMMUNICATION AND COMPUTER TECHNOLOGIES
Transformer-Based Architectures for Robust Speech Recognition and Natural Language Understanding in Noisy and Multilingual Environments
Transformer architectures have already boosted the automatic speech recognition (ASR) and natural language understanding (NLU) fields, and this has resulted in the state-of-the-art performance in capturing various languages and difficult acoustic conditions. The following paper examines how the design and use of transformer variants-Conformer models as well as self-supervised models, i.e, wav2vec 2.0 were modified and used in a specific setting of robust speech processing in noisy and multilingual setups. Our configuration integrates data augmentation with domain adaptation, and together with cross-linguistic learning, focus on boosting the generalization and robustness of the model towards noise. The experiments carried out using benchmark multilingual speech corpora and noisy datasets in the real world reveal that transformer based models perform significantly better than conventional recurrent neural network and convolutional neural networks yielding lower word error rates (WER) and higher semantic accuracy. The findings indicate the success of self-attention mechanisms and convolutional augmentations in the ability to capture both the far and local relationships in a signalled speech. Lastly, the paper presents important issues and areas of future research, such as the creation of low latency inference techniques, model compression techniques toward implementing models on the edge, and ethical concerns related to multilingual speech and language applications. This rich through study can be of great help in promoting efficient and high quality, supportive and scalable transformer based speech and language systems that can be adapted appropriately into real life contexts.
Transformer, Speech Recognition, Natural Language Understanding, Multilingual, Noise Robustness, Conformer, Self-Supervised Learning.
Self-Supervised Audio Representation Learning for Robust Speaker Verification
A new self-supervised learning (SSL) framework of robust speaker verification capable of overcoming the drawbacks of the outdated models of supervised systems, especially when dealing with noisy and domain-mismatched data are introduced in this paper. Experimenting with the large number of unlabeled audio data we use a contrastive learning paradigm to learn extremely discriminative, speaker-specific embeddings with no need to resort to explicit identity labels. This framework includes a convolutional encoder and a transformer-based context network trained over temporally augmented segments of audio to drive the model to learn invariant features to time, noise, and signal Newer to the model. To be more specific, positive updates are done by dynamic telescopic masking, cropping, and augmentation strategies, whereas negative ones are sampled over the batch, thus allowing the system to gain subtle discrimination over various speakers. In contrast to prior SSL frameworks most directly applicable to automatic speech recognition (ASR), our model is explicitly optimized to learn a feature representation that is useful to speaker verification by adding an embedding-level projection and fine-tuning stage that tunes the learnt representations so as to be relevant to speaker identities. Our experiments are performed at large scale on public datasets of speaker verification, such as VoxCeleb1 and VoxCeleb2, both under clean and in the noisy environment. Our approach continually has lower Equal Error Rate (EER) and minimum Detection Cost Function (minDCF) than supervised baselines, such as x-vectors as well as new SSL models, such as wav2vec 2.0 and HuBERT. In addition, the model has high generalization abilities on domain shifts and also retains high performance at low signal-to-noise ratio (SNR) which renders it very appropriate to be applied in real life applications in secure authentication tasks, forensic applications and also in telecommunications. These findings show that self-supervised contrastive learning can be significantly more effective at improving the state-of-the-art in speaker verification with speaker characteristics-sensitive adaptation and less label-intensive. The current work paves the way to exploit large corpora of unlabeled audio data in voice biometric systems and it also provides a firm background to create more research on low-resource speaker recognition systems with privacy awareness.
Self-Supervised Learning (SSL), Speaker Verification, Contrastive Learning, Audio Representation Learning, Voice Biometrics, Deep Neural Networks, Transformer Encoder, Equal Error Rate (EER), Noise Robustness, Unlabeled Audio Data
Lip-Reading-Guided Speech Enhancement via Self-Aligning Cross-Attention Networks
One challenge is speech enhancement in noise, which is of great importance in enhancing communication in real world scenarios like teleconferencing, using hearing aids, and automatic speech recognition (ASR). Although recently developed audio-visual speech processing methods have shown that visual information of the lips motion of a speaker can be used to enhance noise robustness to a high degree, temporal misalignment between the audio and video data has remained a performance limit. In this paper, a novel Lip-Reading-Guided Speech Enhancement architecture is presented relying on Self-Aligning Cross-Attention Networks (SACAN), and serving to dynamically synchronize and allure together multi-modal features to recover clearer speech. The two streams of visual data and audio data are processed through a spatio-temporal convolutional encoder to capture discriminative features of lip movements and are encoded through log-mel spectrogram encoding to achieve representations in spectral and time dimensions. These features are adapted to align frame-wise with a bidirectional self-aligning cross-attention mechanism that helps mitigate distortions caused by latencies and articulation mismatch between modalities. A U-Net based enhancement network is used to decode the fused representation to produce a clean speech spectrogram that in turn is reconstructed into waveform through inverse short-time Fourier transform. The GRID and LRS3-TED datasets are experimented on under three plausible conditions of noise (babble, street and cafe) at various signal-to-noise ratio (SNR) levels. PESQ, STOI, and WER comparisons of quantitative assessments show that SACAN is evaluated to have a PESQ gain of 0.41, a STOI gain of 0.05 and WER reduction of 17.3 percent against state-of-the-art audio-only enhancement baselines. Improved speech naturalness and intelligibility is further verified by subjective listening tests. The results demonstrate the usefulness of cross-modal temporal matching in reliable multimodal speech enhancement and its feasibility to be applied in realtime hostile communication conditions.
Lip-reading, Speech enhancement, Audio-visual fusion, Self-aligning cross-attention networks, Multimodal speech processing, Temporal alignment, Deep learning, Noise-robust ASR
Deep Learning-Driven Speech and Audio Processing: Advances in Noise Reduction, Speech Enhancement, and Real-Time Voice Analytics
The speed of development in deep learning has brought major changes in speech and audio processing, where deep learning today is able to significantly reduce noise, improve speech enhancement, and even allow voice analysis in real time, just to name a few modern applications: smart assistants, hearing aids, web-based call centers, and teleconferencing systems. The paper presented below will have the purpose of examining recent developments in algorithm and deployment methods that pursue the accuracy as well as efficiency issues. The latest architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and self-supervised learning frameworks, are discussed in terms of their potential in dealing with non-stationary noise, maintenance of speech intelligibility, and low-latency processing. The suggested evaluation approach would use benchmark data, such as CHiME, LibriSpeech, and VoiceBank-DEMAND to measure effects on signal-to-noise ratio (SNR) gain, perceptual evaluation of speech quality (PESQ), and word error rate (WER) reduction. We show experimental results that transformer-based enhancement models show up to +11.5 dB SNR improvement and significant PESQ gains on embedded platforms with a processing latency requirement of under 50 ms. The paper also investigates the process of quantization and pruning of lightweight models, making it possible to be deployed to low resources settings. It ends with a discussion of the remaining open problems, including domain adaptation, robustness to multilingual corpora, and ethics, and finally a vision of possible future research areas to close the gap between lab and real-world systems AI-based speech and audio systems back to larger applications.
Deep Learning, Speech Processing, Noise Reduction, Speech Enhancement, Real-Time Voice Analytics, Embedded AI, Audio Signal Processing.
Transformer-Based End-to-End Speech Recognition for Noisy Real-World Environments
New achievements of automatic speech recognition (ASR) have significantly contributed to the creation of control environments, but still, the challenge of ASR implementation in noisy real-life circumstances remains a complicated problem since acoustic interferences, reverberation, and non-stable background noise are broad in a diversified way. The present paper proposes a powerful end-to-end speech recognition system that uses the efficiency of Transformer models to achieve high accuracy of transcriptions even under these difficult acoustic settings. The proposed system also differs with the widespread hybrid HMM-DNN and RNN based systems because; it employs a self-attention-based encoder-decoder architecture that is tailored to operate with long-range dependencies, and also assists in memorizing interacting information, which is required to accomplish recognition in the presence of noise. To be even more robust, the framework incorporates very noise-robust pre-processing methods and generous data augmentation of which augmentation of the spectrum and noise mixing with real-world sources were done during training. The literature conducts extensive testing of the given model on common noisy speech datasets, like CHiME-4, Aurora-4, etc. at various signal-to-noise ratios (SNRs) and acoustic situations to measure its performance. Our Transformer-based ASR system has shown a relative improvement over state-of-the-art RNN and hybrid HMM-DNN models by a significant amount of up to 32 percent on relative word error rate (WER) on the same task, and-at low signal-to-noise-ratio (SNR)-it has shown results comparable to the same evil. On top of that, ablation experiments indicate that data augmentation and self-attention mechanisms are essential to achieving performance improvement when adverse conditions are present. This evidence expounds the revolutionary implication of attention-based models in the progression of strong speech recognition and supports its application potential in real-life environments, such as smart assistants, mobile devices, and embedded systems. The suggested study would open a path towards subsequent researches of lightweight versions of Transformers and multi-modal speech recognition architectures, with the eventual objective of facilitating the possibility of dependable and real-time speech comprehension in progressively fluid and acoustically super busy circumstances.
Speech Recognition, Transformers, End-to-End Models, Noisy Environments, Self-Attention, Data Augmentation, Word Error Rate (WER), Robust ASR
Augmented Reality Audio: Real-Time 3D Sound Localization with Microphone Arrays
There is an emerging trend of applying Augmented Reality (AR) to immersive multisensory experiences, where spatial audio can (and should) be used to provide greater user presence, realism and fidelity of interaction. Sound localization of three-dimensional (3D) spatial sounds with the minimum latency and accuracy is required to harmonic visual and auditory information and create dynamic positioning of a sound source and transmit them into the intricate AR contexts. The paper introduces a real-time 3D sound localization solution integrating wearable technology and small microphone arrays with a hybrid signal processing and deep learning-based pipeline designed to fit wearable augmented reality devices. The suggested algorithm uses Steered Response Power with Phase Transform (SRP-PHAT) robust initial direction-of-arrival (DOA) estimation in a reverberant and noisy environment and a lightweight Convolutional Recurrent Neural Network (CRNN) which improves azimuth and elevation estimation to sub-degree accuracy. The system has a low-latency beamforming system architecture that allows optimal spatial attention, head-tracking compensation with inertial measurement unit (IMU) data to provide spatial coherence with user movement. The proposed approach is the first to balance accuracy, robustness, and real-time responsiveness simultaneously and is thus notable given that traditional localization systems are either plagued with high computational complexity or sub-optimal performance in dynamic acoustic scenarios, and are currently not deployed on mobile AR devices (that have limited processing capabilities). It was experimentally verified on a 32-channel Eigenmike spherical microphone array and also on a 8-element wearable AR headset array, in order to test both high-resolution benchmarking and a real-world deployment scenario. The results show median localization errors of 2.9 degrees in azimuth and 3.8 degrees in elevation and end-to-end processing latency below 25 ms, so meet the requirements of interactive AR audio rendering in terms of perceptual threshold. These results imply that the introduction of high-tech sound localization directly into wearable AR devices, which can support more interactive and perceptually accurate spatial audio environments is possible. It can be used in augmented reality scenarios to provide training, telepresence, assistive hearing devices as well as situational awareness among other potential application areas outside entertainment.
Augmented reality audio, 3D sound localization, microphone arrays, DOA estimation, beamforming, SRP-PHAT, CRNN, real-time spatial audio.
Speech-Based Biometric Authentication for Secure Smart Home Access
As smart home technologies proliferate so quickly, the issue of gaining access to connected environments without losing the convenience of the user becomes a vital concern. The traditional authentication techniques, such as passwords, PINs, and physical tokens, are increasingly being considered inadequate since they can be stolen, spoofed and neglected by users. To this extent, the paper proposes speech-based biometric authentication system that is specifically suited to secure and smooth access of smart home. The suggested solution is advantageous to utilize the individualistic properties of vocal tract features to identify the user by voice, thus giving a non-contact and easy to use option. The use of a hybrid deep learning model (the combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks) will allow better representation of spatial and temporal features of speech signals. Mel-frequency cepstral coefficients (MFCCs) as well as spectral and chromatic features are extracted by the system to form sturdy speaker embeddings. These embeddings are then deployed in real time within an embedded system based on Raspberry Pi that makes direct integration with other IoT-based smart home devices (e.g., door locks, lights, and HVAC controls) possible. The verification accuracy of the proposed model is found with experimental validation in the range of 95.8% achieved and an Equal Error Rate (EER) of 2.3% to demonstrate the proposed model has high resistance to spoofing and replay attacks using benchmark datasets, namely VoxCeleb1 and LibriSpeech. Also, the robust performance of the system is stable across different noise levels and it has sub-50 millisecond inference latency, which can be deployed in real-time on edge devices that have limited resources. The given study indicates the possibility of voice biometrics as a secure, efficient, and scalable authentication technique in the smart home setting, providing better privacy and utility than the traditional approaches. The viable potential of using lightweight and secure biometric authentication projects in real life smart living projects is emphasized by the successful combination of deep learning-driven speech recognition and edge IoT structures.
Smart Home Security; Speaker Verification; Speech Biometrics; CNN-LSTM; MFCC Features; Voice Authentication; Deep Learning; IoT Access Control; Replay Attack Detection; Embedded Edge AI.
Data-Efficient Audio Event Detection with Few-Shot Learning Paradigms
Audio Event Detection (AED) is a critical element of any intelligent system introduced into various applications, e.g., in service of public surveillance, smart home automation as well as assistive technologies. Conventional AED systems are mostly dependent on supervised deep learning models with demanding amounts of labeled data to train, which is typically infeasible because it is time-consuming and laborious in annotating audio objects since almost all occasions require labour-intensive and time-consuming attempts to annotate sounds. To deal with this problem, this paper presents a new data efficient AED framework based on the Few-Shot Learning paradigms (FSL) that allows effective detection with a minimum amount of annotated data. The proposed system utilises a metric-based methodology based on convolutional prototypical networks trained via episodic learning, so it can learn a generalised embedding space based on a small amount of data. It also uses data augmentation to make the training more variable and robust, such as by shifting the pitch, injecting noise, and stretch-time, as well as transfer learning, initialising the model with expert semantic prior knowledge with audio feature extractor networks that have been pre-trained. In a bid to guarantee flexibility and scalability, optimization methods-based FSL approaches are investigated in order to calibrate the model to new classes using minimal gradient updates. The model is tested on two popular benchmark datasets, ESC-50 and UrbanSound8K, on two different classification disciplines, 1-shot and 5-shot classification intervals. Experiments demonstrate that the proposed technique far out-scores established AED baselines and recent meta-learning models, and is able to achieve >74% accuracy on 5-shot scenarios, marking a significant improvement over state-of-the-art baselines with fewer than 20 examples per class. The t-SNE visualizations show evident class-wise separation in the embedding space, which proves the model can discriminate between a wide varieties of audio events. This paper demonstrates how FSL can minimize dependence on the data in AED tasks and, therefore, implement reliable, adaptive audio recognition systems in real-world low-resource settings. The given methodology would be formulating a framework locating scope to scalable AED solutions in recognition of rare, novel, and underrepresented audio incderindlings given limited labeled information.
Audio Event Detection (AED); Few-Shot Learning (FSL); Prototypical Networks; Data-Efficient Learning; Log-Mel Spectrogram; Metric-Based Learning; Self-Supervised Audio; Environmental Sound Classification; Low-Resource Audio Recognition; Meta-Learning.
Explainable Artificial Intelligence in Speech and Audio Processing: Enhancing Interpretability, Fostering Trust, and Addressing Deployment Challenges
The speed at which deep learning has found an application in speech and audio processing has demonstrated significant performance improvements across several applications such as the automatic speech recognition (ASR), speaker verification, emotion recognition, and audio event detection. Nevertheless, the black boxed, opaque nature of state of the art training poses some serious problems of transparency, interpretability, and trust of users, especially in safety critical and privacy sensitive areas. Explainable Artificial Intelligence (XAI) is a way forward in mitigating such problems as it offers inroads into the inner workings of AI systems. This paper works as a review of the existing XAI techniques applied to speech and audio processing and classifies the methods into model-specific and model-agnostic, explaining the most acceptable metrics of their interpretability. We investigate how explainability can increase trustworthiness, promote regulatory compliance, assist with debugging the systems and reduce bias. The issues of deployment, including real-time interpretability in the face of computational constraints, cross-lingual robustness, and human- machine communication, are critically assessed. We also find gaps in the research which can be explored further such as low-latency explanation generation, multimodal explainability and privacy-preserving explanation mechanisms. Lastly, we lay out a roadmap to the inclusion of XAI in next-generation speech and audio systems and ways to promote the deployment of responsible, transparent, and trusted AI in both business and mission-critical scenarios. Three case studies show that XAI is effective in detecting bias, confirming feature explanations and resolving errors in ASR, emotion recognition, and audio event classification.
Explainable AI, Speech Recognition, Audio Processing, Interpretability, Trust, Model Explainability, Deployment Challenges.
Zero-Shot Voice Conversion Using Diffusion Models and Cross-Speaker Embeddings
The paper proposes a brand new zero-shot voice conversion (VC) model that uses the denoising diffusion probabilistic models (DDPM) along with cross-speaker embeddings to produce high quality non-parallel voice conversion that does not involve any speaker specific training. Conventional VC systems, in turn, are traditionally based on parallel corpora or large volumes of speaker-specific data, restricting scalability and transports to unrestricted speakers. By comparison, our model takes advantage of a strong pretrained speaker encoder to learn an efficient representation of cross-speaker embeddings only after only a few seconds of a reference audio. These speaker embeddings are able to represent this speaker-specific prosody and timbre information in a disentangled latent space. At the same time, a content encoder, trained on a pretrained self-supervised automatic speech recognition (ASR) model, extracts speaker invariant is invariant linguistics. The DDPM can then simply produce high-quality audio samples, conditioned also on both content and speaker embeddings, achieved by sequential driving of the noise in the audio samples towards a Gaussian distribution using iteratively defined refinements. In contrast to GAN-based or autoregressive models, diffusion models provide high stability, naturalness and variability in speech generation. We test our model on VCTK and LibriTTS datasets based on both objective measures, including word error rate (WER), speaker verification accuracy, and subjective measures, i.e., tests in terms of mean opinion score (MOS). The performance of our system dramatically improves on both speaker similarity and speech naturalness / intelligibility over previous zero-shot VC baselines, with a MOS of 4.46 and a speaker verification accuracy of 89.7%. Moreover, the given method has high resistance to noise and will be effective even in the case of the perturbations of reference utterances, since it can capture content and voice identity. These findings confirm that cross-speaker embeddings and diffusion-based generation are a viable combination framework to enable zero-shot VC, which is a scalable approach to high quality voice conversion to be applied to precision text-to-speech (TTS), multi-speaker voice dubbing, voice style transfer, and anonymity-preserving voice generation. The suggested architecture is a substantial step on the way to generalizable, data-efficient, and high fidelity voice conversion systems without retraining on new speakers.
Zero-Shot Voice Conversion; Diffusion Models; Cross-Speaker Embeddings; Denoising Diffusion Probabilistic Models (DDPM); Speaker Similarity; Voice Synthesis; Content Preservation; Speaker Identity; ASR Embeddings; Non-Parallel VC
Real-Time Binaural Spatial Audio Rendering Coupled with Dynamic Head Tracking to Improve Accessibility in Augmented and Virtual Reality Environments
AR and VR technologies have developed quickly and provide an extremely compelling visual experience. Nevertheless, it is a matter of concern as efficacy in spatial audio to bring the same immersion and availability is yet not completed, particularly with the users having sensory limitations, including sight or sound inabilities. In this paper, a spatial audio rendering system based on binaural real-time spatial audio incorporating dynamic head tracking, which is intended to increase accessibility in AR/VR, is proposed. It applies personalized Head-Related Transfer Functions (HRTF) and high frequency / low latency inertial measurement data to head track user and provide continuous audio spatialization in real time based on head orientation and position. This dynamic reproduction does not distort the spatial information so that recognition of a source of sound is better, distance can be judged more properly and immersion of listening is enhanced. The framework applies adjustable HRTF profiles and spatial cues enhancement algorithms to support a wide variety of accessibility requirements to assist auditory discrimination and navigation. Large-scale experimental tests with objective tests of localization accuracy and subjective user questionnaires with both neurotypical and visually impaired users prove a high degree of improvement in the perception of spatial audio, decreased cognitive load, and better task performance in an AR/VR environment. The proposed solution is critical to make immersive technologies inclusive and expand the reaches of users with other means. The results reveal the relevance of designing advanced audio rendering coupled with dynamic user response mechanisms to promote accessibility and maximize user experience of an AR/VR system in the future.
Binaural Spatial Audio, Dynamic Head Tracking, Augmented Reality (AR), Virtual Reality (VR), Accessibility in Immersive Systems, Head-Related Transfer Functions (HRTFs)
Robust Speech-to-Text and Text-to-Speech Systems for Noisy and Real-World Acoustic Environments
Strong text-to-speech (TTS), speech-to-text (STT), systems form the basis of strong technologies that support natural human-computer interaction in varying surfaces such as voice assistant, automated transcription, and communication assistance. But they often perform poorly in the real-world acoustic signal-handling scenarios because of background noise, reverberation and channel variability. The following article offers a retrospective overview of the recent developments in creating sound-robust STT and TTS systems paying special attention to latest achievements in state-of-the-art acoustic representation, deep architectures, and data centric approaches like augmentation and domain adaptation. In the case of STT, we look at the new end-to-end models, self-supervised pretraining, and reliable feature extraction which could lead to the increased recognition accuracy in harsh conditions. Regarding TTS, we discuss progress in the research into implementing neural vocoding, model prosody, and adaptive synthesis algorithms with the goal of maintaining the naturalness and intelligibility of speech during playback in noisy conditions. Moreover, we provide associated benchmark datasets and evaluation metrics which help do a rigorous evaluation of the robustness of a system. Just before closing we address key challenges, such as latency, resource limitations, and ethics and present directions of future research towards scaling, low latency, and privacy-conscious speech interfaces that will be deployed in a variety of real-life contexts.
Speech-to-Text, Text-to-Speech, Robust Speech Processing, Noise Reduction, Acoustic Modeling, Real-World Environments, Deep Learning, Speech Enhancement.
Multimodal Emotion Recognition for Human–Robot Interaction Using Speech, Facial Dynamics, and Physiological Signals
Speech emotion recognition is an important research area that makes a significant contribution to the development of socially intelligent HumanMachine Interaction (HRI) since vocal communication contains a lot of paralinguistic information that supplements the semantic load. However, speech based systems can have poor performance in real world situations because of background noise, variant microphone as well as variations in style of speaking. To deal with these shortcomings, we are introducing the multimodal emotion recognition system where the speech processing forms its core and is complemented by face dynamics and physiological measures to increase reliability and precision. The speech channel uses CNN-BiLSTM pipeline to extract spectral-temporal prosodic features of Mel-spectrograms which have strong discriminative power despite noisy environment. A 3D-CNN is used to analyze the facial expressions, and an Electrodermal Activity (EDA), Electrocardiogram (ECG) and Photoplethysmography (PPG) is modeled by using a Temporal Convolutional Network (TCN). Theses modalities are integrated, oriented and harmonized by a Transformer-based cross-attention fusion mechanism that harnesses the complementarity of these strengths to overcome any weaknesses. Simulation of datasets (IEMOCAP, SEMAINE, and AMIGOS) indicate an increment of 7 12 weighted F1-scores compared to unimodal baselines for speech-based HRI scenarios, with or without noise, obscuration, or lost modalities-percentages attesting to the usefulness of the approach in emotion-sensitive HRI.
Multimodal emotion recognition, Human–Robot Interaction (HRI), Speech emotion analysis, Facial dynamics, Physiological signal processing, Cross-attention fusion, Deep learning.
Lightweight Deep Neural Networks for Real-Time Speech Enhancement on Edge Devices
In this paper we are presenting a new architecture of lightweight deep neural networks (DNN) which is targeted to real-time speech enhancement with resource-constrained devices at the edge of the network. With speech-based systems like smart assistants, attachable hearing aids, and voice-integrated interfaces becoming more and more popular, there has been stronger need of high quality noise reduction that would have minimal compute requirements. Analysis using traditional signal processing techniques fails to do well in non-stationary noise conditions, and although deep learning-based techniques are better in this condition, their computational requirements are frequently incompatible with running on low-power edge devices. To solve this, we will present a hybrid architecture that presents an efficient feature extraction framework using depthwise separable convolutions, a short attention-augmented bidirectional GRU model module to gain temporal modelling and post-trained quantization process to allow memory and inference-level compression. The training data afforded by popular benchmark datasets such as VoiceBank-DEMAND and the DNS Challenge corpus contain a wide variety of noise present in test signals as well as noise types. We benchmark the proposed model to several key objective measures of a model including Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and the Signal-To-Distortion Ratio improvement (SDRi), in addition to practical deployment metrics that include model size, inference latency, and power consumption. Our low power DNN has a PESQ of 3.01 and SDRi of 9.4 dB and a run time factor (RTF < 1.0) on both ARM Cortex-M7 microcontrollers using CMSIS-NN and NVIDIA Jetson Nano for both applications using INT8 quantization via TensorRT. The overall size of the model itself is less than 2 MB and the amount of power required falls within the range of battery-powered devices. In objective tests, intelligibility and clarity improvements were also realized under real world hazardous noise situations. The results presented herein indicate that the suggested method is viable in terms of addressing the mismatch between high-performing speech enhancement and embedded hardwares with a scalable and deployable solution to the vast majority of edge AI audio implementations in noisy acoustic situations.
Speech enhancement, edge computing, lightweight DNN, real-time processing, neural quantization, embedded AI.
Non-Intrusive Deep Learning-Based Speech Quality Monitoring for Real-Time Telemedicine Systems
The quality of speech is also significant in the field of telemedicine consultations, and in cases of poor audio quality, it provokes misdiagnoses and patient dissatisfaction, and decreases the efficiency of the process. Conventional intrusive assessment techniques like the PESQ and POLQA use a reference signal, and hence unsuitable in real time telemedicine applications. The paper offers a non-invasive deep learning-based framework of real-time speech quality monitoring within the framework of the telemedicine system. Proposed model uses timefrequency characteristics and transformer-based embeddings to actually predict the Mean Opinion Scores (MOS) without using the clear reference. The low-latency streaming system is designed with minimal delay while being embedded onto a telemedicine platform with WebRTC, allowing continuous feedback to a healthcare provider with respect to the quality. Experiments on the public speech corpus, as well as on telemedicine recordings in a specific domain, prove that the given model yields Pearson correlation of 0.91 with subjective MOS scores whereas the inference latency stays within sub-50 ms on edge infrastructure. The comparative assessments reveal that it has differing accuracy and responsiveness advances to those of the current non-intrusive measures, and makes it suitable in real-time applications of healthcare communication systems.
Telemedicine, Speech Quality Monitoring, Non-Intrusive Assessment, Deep Learning, Mean Opinion Score (MOS), Real-Time Audio Processing, Transformer Models, Audio Quality Evaluation, WebRTC, Healthcare Communication
Multimodal Audio–Visual Fusion for Enhanced Conversational AI and Human–Computer Interaction
The multi modal learning, specifically the audio visual stimulation combination has a big promise to transform conversational artificial intelligence (AI) and human computer interaction (HCI). Current conversational systems generally rely on audio-only systems to process speech and natural language understanding which lacks robustness in environments where there are distractive noise and high dynamic visuality. In the present paper we introduce a multimodal audio-visual synthesis structure that integrates in a common framework the processing of speech data and the visual signs to enhance the perception of speech, recognition of emotion and situational context in interactive systems. The model consists of Convolutional Neural Networks (CNNs) embedded features extraction to recognize the visual information, a Transformer-based acoustic encoder to represent the speech, and a cross-modal attention to combine the temporal and spatial in a dynamic way. The approach was tested on three benchmark datasets (GRID, CREMA-D and LRS3) in order to examine it in both synthetic and real-life settings. The findings indicate that 17.3 percent and 12.8 percent Word Error Rate (WER) and emotion classification accuracy have been reduced in comparison to unimodal baselines. In addition, the system is quite resilient to acoustic interference and visual occlusions and produces robust performance in a wide range of scenarios. The results suggest that the suggested framework may be deployed on the forthcoming conversational systems such as virtual assistants, telepresence robots, and assistive technologies, as well as a scalable backbone that may underlie a future multimodal AI due to its “plug-and-play” nature.
Multimodal Fusion, Audio–Visual Speech Recognition, Conversational AI, Human–Computer Interaction, Cross-Modal Attention.
Hybrid Deep Learning Framework for Acoustic Scene Classification and Environmental Sound Analysis
One of the foundational building blocks of smart audio sensing systems is to automatically recognize and classify acoustic scene, represented by Acoustic Scene Classification (ASC) and Environmental Sound Analysis (ESA) and to facilitate applications of smart surveillance, context-aware computing, and autonomous environmental monitoring. Generalization of the traditional machine learning approaches that work on pre-designed spectral and temporal features has demonstrated moderate success and fail to generalize in heterogeneous and noisy real world situations. A Hybrid Deep Learning Framework proposed in this paper combines the use of Convolutional Neural Networks (CNNs) in extracting spatial features and the use of BiLSTM networks in modeling the temporal sequence. The model performs sequential queries on log-mel spectrograms and has an attention based on prioritization of important acoustic patterns and thus aims to gain discriminative power. Investigations on two benchmark datasets TUT Urban Acoustic Scenes 2018 and ESC-50 show that the proposed method outperforms CNN and LSTM baseline architectures in the classification accuracy and obtains the result of 89.6% for ASC and 88.3% for ESA. Further robustness testing using a variety of signal-to-noise ratios verifies the model cannot be easily and reliably distorted by environmental noise, although performance is slightly compromised as low SNR environments are used. These outcomes reflect the functionality of the framework when being applied to practical deployments requiring superb accuracy and noise resilience. The suggested solution is scalable and generalizable to the task of acoustic signal understanding, and in the future may integrate it into multimodal sensing systems and edge AI applications.
Acoustic Scene Classification, Environmental Sound Analysis, Hybrid Deep Learning, CNN, BiLSTM, Attention Mechanism.
Adaptive Noise Cancellation in Smart Hearing Aids Using Reinforcement Learning
Common noise cancellation algorithms used in current hearing aids,including spectral subtraction, Wiener filtering, and traditional adaptive filtering are prone to performance degradation in non-stationary and dynamically varying speech environments and certainly be in the real world situation which could be a noisy street, a public bus or even at a party. Such approaches are based on predetermined values of adaptation parameters or trained offline, and, therefore, are not able to react efficiently to unpredictable noise properties. In overcoming these impediments, this paper develops an adaptive noise cancellation (ANC) system based on reinforcement learning (RL) that performs continuous, context-aware, real-time noise mitigation in smart hearing aids. The proposed system that involves an RL agent interacting with acoustic environment and being told whether speech clarity and listening comfort improves should make it possible to optimize the approach to noise suppression via trial-and-error learning. A Deep Q-Network (DQN) enables the decision-making process that dynamically updates ANC filter parameters based on a concise state representation based on time frequency features via short-time Fourier transform (STFT), such as, Mel-frequency cepstral coefficients (MFCCs), instantaneous signal-to-noise ratio (SNR), and spectral flatness measures. The reward is a combination of improvements in SNR and perceptual improvements in speech quality (evaluated as perceptual evaluation of speech quality or PESQ), such that the algorithm maximises intelligibility without causing unreasonable distortion. The CHiME-4 noisy speech was used to conduct simulation experiments consisting of real background noise with the case of a street, a cafe, and a transit location. Comparison with Wiener filter, spectral subtractions and a deep speech enhancement baseline that uses a convolutional neural network also illustrates that the proposed RL-based ANC framework improves average SNR with 4.7 dB and STOI by 12.5 percent throughout various noises. These findings indicate the versatility and versatility as well as the possibility of the framework to be individualized depending on the users and hence it fits well as a candidate in future hearing aids that tend to maximize the hearing experience in the most dynamic acoustic tasks.
Adaptive noise cancellation, reinforcement learning, smart hearing aids, deep Q-network, speech enhancement, non-stationary noise.
Continual Learning-Enabled On-Device Audio Event Detection for Low-Latency Edge-IoT Applications
Booming Internet of Things (IoT) applications in smart spaces have led to the need of real-time, accurate, and adaptive audio event detection (AED) engine. Traditional cloud-based AED methods are not always fast during the communication process, they raise privacy issues, and need regular retraining of the model to be accurate in the changing acoustic conditions. In this case study, the design, deployment, and assessment of a continuously learning-empowered on-device AED system has been provided that will be configured to low-latency Edge-IoT workloads. The offered architecture combines lightweight convolutional-recurrent neural network with the continual learning module based on elastic weight consolidation, allowing the model to adapt gradually to new audio events with avoiding catastrophic forgetting. The implementation is on an ARM based edge computing machine and real-life smart manufacturing floor environment with multiple sources of sound, varying noise intensity, and low bandwidth network. The continuous learning strategy on field tests shows increases in event recognition precision by nearly 18 percent over fixed designs when concurrently tested with previously unseen audio classes, with an end-to-end detection latency of <50 ms and a decrease of 92 percent bandwidth usage in contrast to cloud-based instruments. Moreover, on-device processing grants the data privacy requirements uphold and, hence, greatly saves the energy utilized making the system applicable in carrying out IoT nodes that run with batteries. The results indicate the practicality and efficiency of incorporating lifelong learning in an edge-deployed AED system in dynamic resource constrained settings and insight into scaling such architectures to larger smart city, smart healthcare, and smart industrial applications.
Audio Event Detection (AED), Continual Learning, Edge Computing, Internet of Things (IoT), Low-Latency Inference, On-Device Machine Learning
Meta-Learning-Based Few-Shot Speaker Adaptation for Neural Text-to-Speech Synthesis
Neural text-to-speech (TTS) synthesis has also seen the importance of speaker adaptation to produce personalized and natural-sounding speech in an increasingly diverse set of applications, such as voice assistants, audiobooks, or assistive technologies. Nevertheless, the majority of the modern neural TTS systems (like most neural text-to-speech systems) rely on large quantities of high-quality target speaker data and do necessitate retraining of the model, which is impractical in most low-resource conditions. This paper presents a few-shot speaker adaptation mechanism in a meta-learning-based approach that facilitates the high-fidelity voice cloning of processed target speaker voices, with few seconds of target speech. The method uses a model-agnostic meta-learning (MAML) paradigm to learn a universal multi-speaker TTS model that is explicitly optimized to fit, with a small number of fine-tuning steps, to new speakers that have never been seen during training. The given architecture combines a Transformer text encoder, duration and pitch prediction, a HiFi-GAN neural vocoder, and speaker conditioning using d-vector embeddings generated in the set of target speaker samples. The VCTK, LibriTTS and AISHELL-3 datasets are also extensively tested under different few-shot scenarios (5, 10, and 20 utterances) and compared to standard transfer learning and speaker embedding based adaptation baselines. Experimental evidence confirms that the suggested solution beats other current methods every time, resulting in smaller Mel Cepstral Distortion (MCD) and significantly higher Mean Opinion Scores (MOS), with adaptation times taking less than 60 percent smaller. The speaker similarity and naturalness have been confirmed to be higher, in harsh low-resource settings, using subjective listening tests. These results indicate how meta-learning performs at alleviating the data scarcity in TTS speaker adaptation and also reveals its potential in data-sparse settings with resource-limited speech synthesis needs on an ad-hoc basis.
Few-shot learning, speaker adaptation, text-to-speech synthesis, meta-learning, neural vocoder, low-resource TTS.
Graph Neural Network-Driven Acoustic Scene Analysis for Environmental and Urban Sound Monitoring
Urban populations and industrial activities grow rapidly and result in increasingly dynamic and complex acoustic environments, which become a notable hindrance to effective environmental sound monitoring activities. Soundscape analysis is important in helping to characterize soundscapes, and these analyses have been used to detect, categorize and make findings on various noise pollution scenarios, emergency response, transportation, and intelligent city development. Conventional deep learning algorithms, especially convolutional neural networks (CNNs) and recurrent network architectures have had significant successes in acoustic scene classification (ASC) but are intrinsically unable to learn due to the inability to learn non-local dependencies and relational structures contained in the audio features. To overcome these constraints, this paper introduces a Graph Neural Network (GNN) based acoustic scene analysis system that present time--frequency representation of audio signals as graph-structured information where each node represents a segment of spectrum or time and where the edges describe the relationship defined by similarity between the nodes. This suggested GNN structure combines spectral graph convolution networks along with attention-based pooling to attain efficient capture of local, as well as global contextual reliances, within city-based sounds. We perform intensive training on UrbanSound8K and SONYC-UST datasets and include powerful data augmentation techniques allowance, including SpecAugment, Mixup, and adding noise, to allow generalization under low signal-to-noise ratio (SNR) settings. Evaluation vis-a-vis powerful baselines, comprising of VGG-like CNNs and CRNNs, along with spectrogram transformers, shows that our GNN-based technique attains an absolute accuracy advantage of as much as 4.8%, and a steady elevation in F1-score and AUC measures. This framework is highly resistant to environmental noise, scalable to handle large volumes of data as well as adaptable to real-time deployments with IoT-enables applications hence a strong candidate in next generation urban acoustic monitoring environments. Potential future extensions involve multi-modal sensor fusion and self-supervised pretraining with the additional goal of generating more performance in the lower-resource context.
Graph Neural Network (GNN), Acoustic Scene Classification, Environmental Sound Recognition, Urban Noise Monitoring, Spectrogram Graph Representation, Deep Learning, Audio Signal Processing, Smart City, Environmental Acoustics, Urban Sound Dataset.
Case Study: Multimodal Context-Aware Voice Command System for Real-Time Drone Navigation
The paper offers an extensive real life case study on design, development and evaluation of a multi modal context aware voice command based system to aid real-time navigation of unmanned aerial vehicle (UAV). The framework that has been proposed resolves the many drawbacks of traditional drone control systems that incorporate only the use of speech, which has been known to perform poorly when exposed to noisy or visually chaotic conditions. Our system contains an automated speech recognition (ASR) pipeline designed to accommodate the UAV command sets, noise reduction of the environmental acoustic audio via Wiener filtering and spectral embedding, and the use of obstacles detection based on corpus recognition of vision using a YOLOv5 deep learning model. The modalities are integrated into a reasoning engine with a context that can dynamically reconcile voice commands in real-time with sensor data to make safe, contextually accurate navigation decisions. A quadcopter platform with onboard edge-computing hardware implemented the solution and was tested in both indoor and outdoor testbeds at different acoustic (35 75 dB) and lighting environments. The experimental findings show that multimodal system exceeds a speech-only baseline by an average of 6.3 percentage points with voice command recognition accuracies of 92.6 percent when quietness is used and 86.4 percent when high-noise recordings are used, in addition to showing an overall average recognition accuracy of 90.5 percent, compared to recognition of 86.4 percent when only quietness is used and 84.1 percent when only high-noise recordings are used. Additionally, multimodal fusion minimized navigation errors by 32 percent more especially in cases where obstacles were dynamically placed, and the environment were subjected to interference. The results support the conclusion on the usefulness of multimodal levels of speech, vision, and sensors to provide efficient, high-performance, low-latency, and contextual awareness navigation of UAVs. The proposed system presents much promise in its mission-critical applications of search and rescue, field inspection, and human robot working team where common and dependable voice-based control is important.
Voice-Controlled UAV, Automatic Speech Recognition (ASR), Multimodal Fusion, Context-Aware Navigation, Obstacle Detection, Edge Computing
Self-Supervised Learning for Speech and Audio Analytics: A Comprehensive Review of Methods, Applications, and Future Research Directions
Self-Supervised Learning (SSL) has developed fast as a revolutionary method in speech and audio analyses to ensure that supervised learning drawbacks, such as the requirement of sizable, labeled training sets, are overcome. Through pretext tasks, such as masked prediction, contrastive learning, and reconstruction, this review critically discusses methods of SSL that take advantage of inherent structures in the audio signal. We critically evaluate state-of-art paradigms, including wav2vec 2.0, HuBERT, BYOL-A and data2vec, explaining their design specifications in architecture, training and evaluation benchmark results in tasks like automatic speech recognition, speaker verification, emotion recognition, and music information retrieval. A comparative analysis points at the trade-offs between the accuracy, computation performance, and domain adaptability. New directions are also discussed, including multimodal SSL to combine audio with visual and textual input and federated SSL to allow privacy-preserving learning and edge-optimized SSL to run on low-power devices. The review finally proposes the strategic directions to follow on improving the SSL in the real world application by noting the research challenges (such as scalability, cross-lingual generalization, and interpretability) which are a major concern. The current synthesis is to help scholars and practitioners to pursue the ultimate goal of creating efficient, effective, and ethically consistent SSL machines in response to the maturing world of speech and audio.
Self-Supervised Learning, Speech Analytics, Audio Representation Learning, wav2vec, HuBERT, Edge AI, Multimodal Learning
Music Information Retrieval and Audio Content Processing in the Age of Artificial Intelligence: Techniques, Challenges, and Emerging Applications
The field of Music Information Retrieval (MIR) and audio content processing have become an important area of research concern in the age of Artificial Intelligence (AI), responding to a need to make the ever-increasing music archives stored and recovered automatically through analysis, indexing and search. In this research, we attempt to collate an all-inclusive review of the AI approaches that have changed the face of the establish MIR tasks such as genre classification, instrument identification, mood identification and the music suggestion. It discusses recent methods of feature extraction, including the use of Mel-Frequency Cepstral Coefficients (MFCCs) and deep spectral embeddings, and representation learning methods made available by convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The review of the recent work shows that AI models with their dramatic superiority over traditional approaches to relying on handcrafted features open great opportunities regarding accuracy, generalization, and robustness in a broad range of datasets. Crucial issues, including the scarcity of labeled data, domain adaptation, model interpretability, and the intellectual property are critically addressed. There are also examples of emerging applications covered by the paper such as AI-aided music composition, adaptive streaming and real time audio analytics in interactive systems. The review ends by providing future research orientations based on explainable AI, multimodal combination of role, audio-lyric-visual data input and deployment of resources on low-powered edge devices. This synthesis can be used as a reference to researchers and practitioners alike in the industry that seeks to create scalable, accurate, and ethically responsible MIR systems during times of the AI revolution.
Music Information Retrieval, Audio Content Processing, Artificial Intelligence, Deep Learning, Signal Processing, Music Recommendation, Automatic Music Transcription, Audio Feature Extraction.
Harmonic Feature Extraction and Deep Fusion Networks for Music Genre Classification
Music Genre Classification is an important job in the music information retrieval systems from recommendation engines to digital music archive. Traditional machine learning approaches are dominated by the usage of handcrafted features which are often inadequate to the complex hierarchical structure of music. This paper presents an innovative hybrid framework for the extraction of harmonic features using a deep fusion network architecture for genre identification of music and accurate and robust classification. The method that is being proposed first extracts HPSS based features,.statistics CENS ; Mel spectrograms and MF CCs to capture the low level and harmonic content. These are then fused with a dual-branch deep neural network fusing Convolutional Neural Networks (CNN) for spatial features extraction and Bidirectional Gated Recurrent Units (Bi-GRUs) for temporal sequence modeling. The fusion module fuses the both learned representations with an attention based mechanism. Based on experiment conducted on the GTZAN and FMA dataset, it is shown that our proposed framework outperforms several state-of-the-art models with classification accuracies of 93.6% and 89.1% respectively. This work proves the efficiency of harmonic-aware deep fusion networks in representing both spectral and temporal dynamics for music genre classification.
Robust Audio Signal Enhancement Using Hybrid Spectral-Temporal Deep Learning Models in Noisy Environments
Improvement of the audio signal is highly essential in many applications starting with telecommunication and up to assisting hearing devices in difficult noisy settings. In this paper, we propose a hybrid spectral-temporal deep learning model combining the convolutional and recurrent neural network model for enhancing robust audio signals. Spectral representations of the audio (log-magnitude spectrograms) and temporal dependencies were used in the model via bidirectional gated recurrent units (Bi-GRU). A multi-stage architecture is selected wherein the CNN effects spatial features and the Bi-GRU the temporal continuity. Utilized over databases including VoiceBank-DEMAND and TIMIT with artificially corrupted noises at different SNR levels (0 dB, 5 dB, 10 dB), the proposed model has been proven to dramatically increase the quality of the signal and gains PESQ up to 3.21 and STOI increments up to 0.26 over classical & modern deep models. This shows that hybrid deep learning works in real-world noisy set-ups.
Voice Command Recognition for Smart Home Assistants Using Few-Shot Learning Techniques
Voice command recognition is an essential part of intelligent and personalized development of the smart home assistants. Nevertheless, conventional deep learning techniques demand intensive labeled data and computational resources, and thus limiting the applicability in situations where commands are rare or personalized. This paper proposes a new few-shot learning framework designed for practical voice command recognition of smart homes. With the help of prototypical networks and data augmentation algorithms, our approach deals effectively with a small number of training examples. We test our model on data from the Google Speech Commands and a custom command collection for smart home applications. Results achieve Top-1 accuracy of 89.3% using just five examples per class besting baseline convolutional neural networks and other few-shot variants. On embedded systems, our framework can be deployed in a real-time manner, owing to the low latency it entails. This research offers a fresh opportunity to establish tree-specific voice control in smart houses with little training of the human user.
Lightweight Speaker Identification Framework Using Deep Embeddings for Real-Time Voice Biometrics
Voice biometric systems are based on speaker identification that is important for the secure and personalized human–machine interaction. But deploying reactive and reliable speaker recognition models on memory, delay, and computationally constrained edge devices still have been a problem. In this paper, we propose a lightweight speaker identification framework built on top of deep speaker embeddings, produced by a considerably smaller convolutional neural network (CNN) architecture. We use a time delayed CNN front end to extract a fixed length embedding from a variable length utterance and use average pooling and cosine similarity based classifier for low latency inference. Additionally, quantization aware training and pruning methods are used to optimize for performance at runtime, significantly reducing the model size and over 60% while retaining accuracy. The proposed model is evaluated on VoxCeleb1 and a custom low resource dataset where identified with a Top-1 accuracy of 94.2% and 92.5% respectively; with inference latency under 30 milliseconds on Raspberry Pi 4. In these results show that it is practical to run deep embedding speaker ID on embedded platforms, but with the robustness and precision retained.
Environmental Sound Classification Using CNNs with Frequency-Attentive Acoustic Modeling
With urban monitoring, smart surveillance, and context aware mobile computing being just some real world applications, environmental sound classification is an essential problem to solve. But recognizing diverse sound events under multiple noise and acoustic conditions accurately is an open problem. In this paper, we propose a novel convolutional neural network (CNN) framework with frequency-attentive acoustic modeling to boost classification accuracy and robustness in the noisy environments. We present an approach that proposes a spectral attention module in order to highlight discriminative frequency bands in the logmel spectrogram, so that the network could pay attention to informative spectrogram patterns that are unique to certain sound classes. Results on ESC-50 and UrbanSound8K datasets further demonstrate that the proposed model has state-of-theart performance in terms of classification accuracy of 89.4% and 87.1% respectively, outperforming several existing CNNbased baselines. Ablation studies show that the role of the attention mechanism is responsible for noise resilience and generalization. Overall, this research provides a lightweight but powerful ESC solution at the cost of accuracy to maintain computational efficiency such that it is viable for deployment to edge audio recognition systems.
Real-Time Speech Enhancement on Edge Devices Using Optimized Deep Learning Models
With edge computing increasing becoming a popular implementation for real-time processing and storing data, voice driven applications need effective and lightweight Speech Enhancement Systems to meet real-time processing requirement on constrained hardware platforms. In this work, we propose a deep learning – based speech enhancement framework optimized for the real – time improvement of speech quality on the edge. The system proposed in this thesis contains a convolutional recurrent neural network (CRNN) architecture with pruning and quantization techniques that reduce complexity of the model without reducing performance. The experiments were conducted extensively across the TIMIT and DNS Challenge datasets with different types of noises. We finally show that the model achieves PESQ score of 3.4, 9.1 dB SNR gain over baseline models while maintaining an inference latency under 25ms on ARM Cortex-A53 processors. The results show that the proposed model suitably compromises accuracy, latency, and resource efficiency, hence making it a suitable component for real time applications, for instance, hearing aids, smart assistants, and mobile devices.