A Lightly Supervised Approach to Detect Stuttering in Children's Speech
| Autor: | Sadeen Alharbi, Madina Hasan, Anthony J. H. Simons, Shelagh Brumfitt, Phil D. Green |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
Stuttering Phrase Repetition (rhetorical device) Computer science media_common.quotation_subject Speech recognition 020206 networking & telecommunications 02 engineering and technology Task (project management) 030507 speech-language pathology & audiology 03 medical and health sciences Reading (process) 0202 electrical engineering electronic engineering information engineering medicine Speech disorder medicine.symptom 0305 other medical science Association (psychology) Speech-Language Pathology media_common |
| Zdroj: | INTERSPEECH |
| DOI: | 10.21437/interspeech.2018-2155 |
| Popis: | © 2018 International Speech Communication Association. All rights reserved. In speech pathology, new assistive technologies using ASR and machine learning approaches are being developed for detecting speech disorder events. Classically-trained ASR model tends to remove disfluencies from spoken utterances, due to its focus on producing clean and readable text output. However, diagnostic systems need to be able to track speech disfluencies, such as stuttering events, in order to determine the severity level of stuttering. To achieve this, ASR systems must be adapted to recognise full verbatim utterances, including pseudo-words and non-meaningful part-words. This work proposes a training regime to address this problem, and preserve a full verbatim output of stuttering speech. We use a lightly-supervised approach using task-oriented lattices to recognise the stuttering speech of children performing a standard reading task. This approach improved the WER by 27.8% relative to a baseline that uses word-lattices generated from the original prompt. The improved results preserved 63% of stuttering events (including sound, word, part-word and phrase repetition, and revision). This work also proposes a separate correction layer on top of the ASR that detects prolongation events (which are poorly recog-nised by the ASR). This increases the percentage of preserved stuttering events to 70%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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