An End-To-End Approach To Automatic Speech Assessment For People With Aphasia

Keywords

Cantonese; End-to-end; Pathological speech assessment

Abstract

Conventionally, 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-to-one GRU-RNN in this specific task.

Publication Date

7-2-2018

Publication Title

2018 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018 - Proceedings

Number of Pages

66-70

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ISCSLP.2018.8706690

Socpus ID

85065880011 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85065880011

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