Search Results for author:
Found 29 papers, 7 papers with code
MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition
This paper proposes a novel Mixture of Prompt-Experts based Speaker Adaptation approach (MOPSA) for elderly speech recognition.
Structured Speaker-Deficiency Adaptation of Foundation Models for Dysarthric and Elderly Speech Recognition
Experiments on the UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest structured speaker-deficiency adaptation of HuBERT and Wav2vec2-conformer models consistently outperforms baseline SFMs using either: a) no adapters; b) global adapters shared among all speakers; or c) single attribute adapters modelling speaker or deficiency labels alone by statistically significant WER reductions up to 3. 01% and 1. 50% absolute (10. 86% and 6. 94% relative) on the two tasks respectively.
A Comparative Study of Discrete Speech Tokens for Semantic-Related Tasks with Large Language Models
With the rise of Speech Large Language Models (Speech LLMs), there has been growing interest in discrete speech tokens for their ability to integrate with text-based tokens seamlessly.
Improving Grapheme-to-Phoneme Conversion through In-Context Knowledge Retrieval with Large Language Models
Using GPT-4 in the ICKR system can increase of 3. 5% absolute (3. 8% relative) on the Librig2p dataset.
Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTC
SACTC is a tailored CTC variant for multi-talker scenarios, it explicitly models speaker disentanglement by constraining the encoder to represent different speakers' tokens at specific time frames.
Exploring SSL Discrete Speech Features for Zipformer-based Contextual ASR
Self-supervised learning (SSL) based discrete speech representations are highly compact and domain adaptable.
Exploring SSL Discrete Tokens for Multilingual ASR
With the advancement of Self-supervised Learning (SSL) in speech-related tasks, there has been growing interest in utilizing discrete tokens generated by SSL for automatic speech recognition (ASR), as they offer faster processing techniques.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition
Experiments are conducted on four tasks: the English UASpeech and TORGO dysarthric speech corpora; and the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets.
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement
Notably, ASR models trained on GigaSpeech 2 can reduce the word error rate for Thai, Indonesian, and Vietnamese on our challenging and realistic YouTube test set by 25% to 40% compared to Whisper large-v3, with merely 10% model parameters.
Towards Effective and Efficient Non-autoregressive Decoding Using Block-based Attention Mask
This paper proposes a novel non-autoregressive (NAR) block-based Attention Mask Decoder (AMD) that flexibly balances performance-efficiency trade-offs for Conformer ASR systems.
One-pass Multiple Conformer and Foundation Speech Systems Compression and Quantization Using An All-in-one Neural Model
We propose a novel one-pass multiple ASR systems joint compression and quantization approach using an all-in-one neural model.
Cross-Speaker Encoding Network for Multi-Talker Speech Recognition
To the best of our knowledge, this work represents an early effort to integrate SIMO and SISO for multi-talker speech recognition.
Audio-visual End-to-end Multi-channel Speech Separation, Dereverberation and Recognition
Accurate recognition of cocktail party speech containing overlapping speakers, noise and reverberation remains a highly challenging task to date.
Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems
Rich sources of variability in natural speech present significant challenges to current data intensive speech recognition technologies.
Towards Effective and Compact Contextual Representation for Conformer Transducer Speech Recognition Systems
Current ASR systems are mainly trained and evaluated at the utterance level.
Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator
Extending on this, we incorporate a diarization branch into the Sidecar, allowing for unified modeling of both ASR and diarization with a negligible overhead of only 768 parameters.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Use of Speech Impairment Severity for Dysarthric Speech Recognition
A key challenge in dysarthric speech recognition is the speaker-level diversity attributed to both speaker-identity associated factors such as gender, and speech impairment severity.
Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition
Experiments conducted on the UASpeech dysarthric and DementiaBank Pitt elderly speech corpora suggest TDNN and Conformer ASR systems integrated domain adapted wav2vec2. 0 models consistently outperform the standalone wav2vec2. 0 models by statistically significant WER reductions of 8. 22% and 3. 43% absolute (26. 71% and 15. 88% relative) on the two tasks respectively.
A Sidecar Separator Can Convert a Single-Talker Speech Recognition System to a Multi-Talker One
Although automatic speech recognition (ASR) can perform well in common non-overlapping environments, sustaining performance in multi-talker overlapping speech recognition remains challenging.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems
Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Confidence Score Based Conformer Speaker Adaptation for Speech Recognition
A key challenge for automatic speech recognition (ASR) systems is to model the speaker level variability.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection
Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression.
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems
Fundamental modelling differences between hybrid and end-to-end (E2E) automatic speech recognition (ASR) systems create large diversity and complementarity among them.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition
Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems designed for normal speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition
Articulatory features are inherently invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition (ASR) systems for normal speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Recent Progress in the CUHK Dysarthric Speech Recognition System
Despite the rapid progress of automatic speech recognition (ASR) technologies in the past few decades, recognition of disordered speech remains a highly challenging task to date.
Audio-Visual Speech Recognition
Automatic Speech Recognition
+4
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
State-of-the-art automatic speech recognition (ASR) system development is data and computation intensive.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System
A multi-turn dialogue always follows a specific topic thread, and topic shift at the discourse level occurs naturally as the conversation progresses, necessitating the model's ability to capture different topics and generate topic-aware responses.
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks
Deep neural networks (DNNs) based automatic speech recognition (ASR) systems are often designed using expert knowledge and empirical evaluation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
