An End-to-End Approach to Automatic Speech Assessment for People with Aphasia
Refereed conference paper presented and published in conference proceedings

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AbstractConventionally, automatic assessment of pathological speech involves two main steps: (1) extraction of pathology-specific features; (2) classification or regression of extracted features. Given the great variety of speech and language disorders, feature design is never a straightforward task, and yet it is most critical to the performance of assessment. This paper presents an end-to-end approach to automatic speech assessment for Cantonese-speaking people with aphasia (PWA). The assessment is formulated as a binary classification problem to differentiate PWA with high scores of subjective assessment from those with low scores. The sequence-to-one GRU-RNN and CNN models are applied to realize the end-to-end mapping from speech signals to the classification result. The speech features used for assessment are learned implicitly by the neural network model. Preliminary experimental results show that the end-to-end approach could reach a performance level comparable to conventional two-step approach. The experimental results also suggest that CNN performs better than sequence-toone GRU-RNN in this specific task.
All Author(s) ListYing Qin, Tan Lee, Yuzhong Wu, Anthony Pak Hin Kong
Name of Conference11th International Symposium on Chinese Spoken Language Processing (ISCSLP)
Start Date of Conference26/11/2018
End Date of Conference29/11/2018
Place of ConferenceTaipei
Country/Region of ConferenceTaiwan
Proceedings Title2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP)
Place of PublicationTaipei
Pages66 - 70
LanguagesEnglish-United States
KeywordsPathological speech assessment, end-to-end, Cantonese

Last updated on 2021-10-05 at 01:36