Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2305.10659 (eess)
[Submitted on 18 May 2023]
Title:Use of Speech Impairment Severity for Dysarthric Speech Recognition
Authors:Mengzhe Geng,Zengrui Jin,Tianzi Wang,Shujie Hu,Jiajun Deng,Mingyu Cui,Guinan Li,Jianwei Yu,Xurong Xie,Xunying Liu
View a PDF of the paper titled Use of Speech Impairment Severity for Dysarthric Speech Recognition, by Mengzhe Geng and 9 other authors
View PDFAbstract: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. Most prior researches on addressing this issue focused on using speaker-identity only. To this end, this paper proposes a novel set of techniques to use both severity and speaker-identity in dysarthric speech recognition: a) multitask training incorporating severity prediction error; b) speaker-severity aware auxiliary feature adaptation; and c) structured LHUC transforms separately conditioned on speaker-identity and severity. Experiments conducted on UASpeech suggest incorporating additional speech impairment severity into state-of-the-art hybrid DNN, E2E Conformer and pre-trained Wav2vec 2.0 ASR systems produced statistically significant WER reductions up to 4.78% (14.03% relative). Using the best system the lowest published WER of 17.82% (51.25% on very low intelligibility) was obtained on UASpeech.
| Comments: | Accepted to INTERSPEECH2023 |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD) |
| Cite as: | arXiv:2305.10659 [eess.AS] |
| (orarXiv:2305.10659v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2305.10659 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Use of Speech Impairment Severity for Dysarthric Speech Recognition, by Mengzhe Geng and 9 other authors
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