Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation

• URL: http://arxiv.org/abs/2407.18461v1
• Date: Fri, 26 Jul 2024 02:03:23 GMT
• Title: Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based Adaptation
• Authors: Shiyao Wang, Shiwan Zhao, Jiaming Zhou, Aobo Kong, Yong Qin,
• Abstract summary: Dysarthric speech recognition (DSR) presents a formidable challenge due to inherent inter-speaker variability. Traditional speaker adaptation methodologies typically involve fine-tuning models for each speaker. We introduce a prototype-based approach that markedly improves DSR performance for unseen dysarthric speakers without additional fine-tuning.
• Score: 10.014408469561834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dysarthric speech recognition (DSR) presents a formidable challenge due to inherent inter-speaker variability, leading to severe performance degradation when applying DSR models to new dysarthric speakers. Traditional speaker adaptation methodologies typically involve fine-tuning models for each speaker, but this strategy is cost-prohibitive and inconvenient for disabled users, requiring substantial data collection. To address this issue, we introduce a prototype-based approach that markedly improves DSR performance for unseen dysarthric speakers without additional fine-tuning. Our method employs a feature extractor trained with HuBERT to produce per-word prototypes that encapsulate the characteristics of previously unseen speakers. These prototypes serve as the basis for classification. Additionally, we incorporate supervised contrastive learning to refine feature extraction. By enhancing representation quality, we further improve DSR performance, enabling effective personalized DSR. We release our code at https://github.com/NKU-HLT/PB-DSR.

Related papers

• Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition [71.87998918300806]
  This paper explores approaches to integrate domain fine-tuned SSL pre-trained models and their features into TDNN and Conformer ASR systems. TDNN systems constructed by integrating domain-adapted HuBERT, wav2vec2-conformer or multi-lingual XLSR models consistently outperform standalone fine-tuned SSL pre-trained models. Consistent improvements in Alzheimer's Disease detection accuracy are also obtained using the DementiaBank Pitt elderly speech recognition outputs.
  arXiv  Detail & Related papers  (2024-07-03T08:33:39Z)
• Speaker-Independent Dysarthria Severity Classification using Self-Supervised Transformers and Multi-Task Learning [2.7706924578324665]
  This study presents a transformer-based framework for automatically assessing dysarthria severity from raw speech data. We develop a framework, called Speaker-Agnostic Latent Regularisation (SALR), incorporating a multi-task learning objective and contrastive learning for speaker-independent multi-class dysarthria severity classification. Our model demonstrated superior performance over traditional machine learning approaches, with an accuracy of $70.48%$ and an F1 score of $59.23%$.
  arXiv  Detail & Related papers  (2024-02-29T18:30:52Z)
• Extending Whisper with prompt tuning to target-speaker ASR [18.31992429200396]
  Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech of a target speaker from overlapped utterances. Most of the existing target-speaker ASR (TS-ASR) methods involve either training from scratch or fully fine-tuning a pre-trained model. This work leverages prompt tuning, a parameter-efficient fine-tuning approach, to extend Whisper, a large-scale single-talker ASR model, to TS-ASR.
  arXiv  Detail & Related papers  (2023-12-13T11:49:16Z)
• DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification [55.306583814017046]
  We present a novel difficulty-aware semantic augmentation (DASA) approach for speaker verification. DASA generates diversified training samples in speaker embedding space with negligible extra computing cost. The best result achieves a 14.6% relative reduction in EER metric on CN-Celeb evaluation set.
  arXiv  Detail & Related papers  (2023-10-18T17:07:05Z)
• Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition [66.94463981654216]
  We propose prompt tuning methods of Deep Neural Networks (DNNs) for speaker-adaptive Visual Speech Recognition (VSR) We finetune prompts on adaptation data of target speakers instead of modifying the pre-trained model parameters. The effectiveness of the proposed method is evaluated on both word- and sentence-level VSR databases.
  arXiv  Detail & Related papers  (2023-02-16T06:01:31Z)
• ASR-Aware End-to-end Neural Diarization [15.172086811068962]
  We present a Conformer-based end-to-end neural diarization (EEND) model that uses both acoustic input and features derived from an automatic speech recognition (ASR) model. Three modifications to the Conformer-based EEND architecture are proposed to incorporate the features. Experiments on the two-speaker English conversations of Switchboard+SRE data sets show that multi-task learning with position-in-word information is the most effective way of utilizing ASR features.
  arXiv  Detail & Related papers  (2022-02-02T21:17:14Z)
• Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction [109.44933866397123]
  Noise robustness is essential for deploying automatic speech recognition systems in real-world environments. We employ a noise-robust representation learned by a refined self-supervised framework for noisy speech recognition. We achieve comparable performance to the best supervised approach reported with only 16% of labeled data.
  arXiv  Detail & Related papers  (2021-10-28T20:39:02Z)
• Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech [5.960279280033886]
  We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to model fine-tuning. We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.
  arXiv  Detail & Related papers  (2021-09-14T20:04:47Z)
• Feature Replacement and Combination for Hybrid ASR Systems [47.74348197215634]
  We investigate the usefulness of one of these front-end frameworks, namely wav2vec, for hybrid ASR systems. In addition to deploying a pre-trained feature extractor, we explore how to make use of an existing acoustic model (AM) trained on the same task with different features. We obtain a relative improvement of 4% and 6% over our previous best model on the LibriSpeech test-clean and test-other sets.
  arXiv  Detail & Related papers  (2021-04-09T11:04:58Z)
• Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
  This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features. We extract a speaker representation used for adaptation directly from the test utterance.
  arXiv  Detail & Related papers  (2020-02-14T05:05:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.