Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.09329 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914834004574208 |
|---|---|
| author | Chowdary, Paleti Nikhil Aravind, Vadlapudi Sai Vardhan, Gorantla V N S L Vishnu Akshay, Menta Sai Aashish, Menta Sai G, Jyothish Lal. |
| author_facet | Chowdary, Paleti Nikhil Aravind, Vadlapudi Sai Vardhan, Gorantla V N S L Vishnu Akshay, Menta Sai Aashish, Menta Sai G, Jyothish Lal. |
| contents | Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Detection of dysarthria is important for several reasons as it can be used to develop a treatment plan and help improve a person's quality of life and ability to communicate effectively. Much of the literature focused on improving ASR systems for dysarthric speech. The objective of the current work is to develop models that can accurately classify the presence of dysarthria and also give information about the intelligibility level using limited data by employing a few-shot approach using a transformer model. This work also aims to tackle the data leakage that is present in previous studies. Our whisper-large-v2 transformer model trained on a subset of the UASpeech dataset containing medium intelligibility level patients achieved an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and specificity of 0.91. Experimental results also demonstrate that the model trained using the 'words' dataset performed better compared to the model trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model achieved an accuracy of 67%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09329 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers Chowdary, Paleti Nikhil Aravind, Vadlapudi Sai Vardhan, Gorantla V N S L Vishnu Akshay, Menta Sai Aashish, Menta Sai G, Jyothish Lal. Sound Computation and Language Audio and Speech Processing Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Detection of dysarthria is important for several reasons as it can be used to develop a treatment plan and help improve a person's quality of life and ability to communicate effectively. Much of the literature focused on improving ASR systems for dysarthric speech. The objective of the current work is to develop models that can accurately classify the presence of dysarthria and also give information about the intelligibility level using limited data by employing a few-shot approach using a transformer model. This work also aims to tackle the data leakage that is present in previous studies. Our whisper-large-v2 transformer model trained on a subset of the UASpeech dataset containing medium intelligibility level patients achieved an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and specificity of 0.91. Experimental results also demonstrate that the model trained using the 'words' dataset performed better compared to the model trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model achieved an accuracy of 67%. |
| title | A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2309.09329 |