Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO

• URL: http://arxiv.org/abs/2411.00980v2
• Date: Fri, 08 Nov 2024 00:36:10 GMT
• Title: Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO
• Authors: Macarious Hui, Jinda Zhang, Aanchan Mohan,
• Abstract summary: Individuals with cerebral palsy (CP) and amyotrophic lateral sclerosis (ALS) frequently face challenges with articulation, leading to dysarthria and resulting in atypical speech patterns. We found that state-of-the-art (SOTA) automatic speech recognition (ASR) technology like Whisper and Wav2vec2.0 marginalizes atypical speakers largely due to the lack of training data. Our work looks to leverage SOTA ASR followed by domain specific error-correction.
• Score: 0.13108652488669734
• License:
• Abstract: Individuals with cerebral palsy (CP) and amyotrophic lateral sclerosis (ALS) frequently face challenges with articulation, leading to dysarthria and resulting in atypical speech patterns. In healthcare settings, communication breakdowns reduce the quality of care. While building an augmentative and alternative communication (AAC) tool to enable fluid communication we found that state-of-the-art (SOTA) automatic speech recognition (ASR) technology like Whisper and Wav2vec2.0 marginalizes atypical speakers largely due to the lack of training data. Our work looks to leverage SOTA ASR followed by domain specific error-correction. English dysarthric ASR performance is often evaluated on the TORGO dataset. Prompt-overlap is a well-known issue with this dataset where phrases overlap between training and test speakers. Our work proposes an algorithm to break this prompt-overlap. After reducing prompt-overlap, results with SOTA ASR models produce extremely high word error rates for speakers with mild and severe dysarthria. Furthermore, to improve ASR, our work looks at the impact of n-gram language models and large-language model (LLM) based multi-modal generative error-correction algorithms like Whispering-LLaMA for a second pass ASR. Our work highlights how much more needs to be done to improve ASR for atypical speakers to enable equitable healthcare access both in-person and in e-health settings.

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)
• Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation [0.0]
  A critical barrier to progress is the scarcity of large, annotated disfluent speech datasets. We present an inclusive ASR design approach, leveraging self-supervised learning on standard speech followed by targeted fine-tuning and data augmentation. Results show that fine-tuning wav2vec 2.0 with even a relatively small, labeled dataset, alongside data augmentation, can significantly reduce word error rates for disfluent speech.
  arXiv  Detail & Related papers  (2024-06-14T16:56:40Z)
• UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization [60.43992089087448]
  Dysarthric speech reconstruction systems aim to automatically convert dysarthric speech into normal-sounding speech. We propose a Unit-DSR system, which harnesses the powerful domain-adaptation capacity of HuBERT for training efficiency improvement. Compared with NED approaches, the Unit-DSR system only consists of a speech unit normalizer and a Unit HiFi-GAN vocoder, which is considerably simpler without cascaded sub-modules or auxiliary tasks.
  arXiv  Detail & Related papers  (2024-01-26T06:08:47Z)
• Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition [55.25565305101314]
  Articulatory features are invariant to acoustic signal distortion and have been successfully incorporated into automatic speech recognition systems. This paper presents a cross-domain and cross-lingual A2A inversion approach that utilizes the parallel audio and ultrasound tongue imaging (UTI) data of the 24-hour TaL corpus in A2A model pre-training. Experiments conducted on three tasks suggested incorporating the generated articulatory features consistently outperformed the baseline TDNN and Conformer ASR systems.
  arXiv  Detail & Related papers  (2022-06-15T07:20:28Z)
• Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition [30.885165674448352]
  This paper presents a novel set of speaker dependent (GAN) based data augmentation approaches for elderly and dysarthric speech recognition. GAN based data augmentation approaches consistently outperform the baseline speed perturbation method by up to 0.91% and 3.0% absolute. Consistent performance improvements are retained after applying LHUC based speaker adaptation.
  arXiv  Detail & Related papers  (2022-05-13T04:29:49Z)
• Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation [59.41186714127256]
  Dysarthric speech reconstruction (DSR) aims to improve the quality of dysarthric speech. Speaker encoder (SE) optimized for speaker verification has been explored to control the speaker identity. We propose a novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA)
  arXiv  Detail & Related papers  (2022-02-18T08:59:36Z)
• An Approach to Improve Robustness of NLP Systems against ASR Errors [39.57253455717825]
  Speech-enabled systems typically first convert audio to text through an automatic speech recognition model and then feed the text to downstream natural language processing modules. The errors of the ASR system can seriously downgrade the performance of the NLP modules. Previous work has shown it is effective to employ data augmentation methods to solve this problem by injecting ASR noise during the training process.
  arXiv  Detail & Related papers  (2021-03-25T05:15:43Z)
• Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition [62.94773371761236]
  We consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate. We propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. Our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER)
  arXiv  Detail & Related papers  (2021-03-12T10:10:13Z)
• Robust Prediction of Punctuation and Truecasing for Medical ASR [18.08508027663331]
  This paper proposes a conditional joint modeling framework for prediction of punctuation and truecasing. We also present techniques for domain and task specific adaptation by fine-tuning masked language models with medical domain data.
  arXiv  Detail & Related papers  (2020-07-04T07:15:13Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

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.