Automatic evaluation of hypernasality and speech intelligibility for children with cleft palate

He, L, Zhang, J, Liu, Q, Yin, H and Lech, M 2013, 'Automatic evaluation of hypernasality and speech intelligibility for children with cleft palate', in Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA 2013), Melbourne, Australia, 19-21 June 2013, pp. 220-223.


Document type: Conference Paper
Collection: Conference Papers

Title Automatic evaluation of hypernasality and speech intelligibility for children with cleft palate
Author(s) He, L
Zhang, J
Liu, Q
Yin, H
Lech, M
Year 2013
Conference name ICIEA 2013
Conference location Melbourne, Australia
Conference dates 19-21 June 2013
Proceedings title Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA 2013)
Publisher IEEE
Place of publication United States
Start page 220
End page 223
Total pages 4
Abstract The speech of cleft palate (CP) patients has typical characteristics. Hypernasality and low speech intelligibility are the primary characteristics for CP speech. In this work, an automatic evaluation of different levels of hypernasality and speech intelligibility algorithm for CP speech was proposed, in order to provide an objective tool for speech therapist. To identify different levels of hypernasality, the short-time energy and Mel frequency cepstral coefficients were calculated as acoustic features, then Gaussian mixture model was applied as classifier. For the automatic speech intelligibility evaluation, the classical automatic isolated word recognition system was applied. The automatic speech recognition accuracy could be viewed as an indicator for various levels of speech intelligibility. The experiment results indicated that the proposed computer-based system achieved a good performance on the automatic classification of CP hypernasality and speech intelligibility levels. The average classification accuracy was over 79% for four types of hypernasality detection, and the automatic speech recognition accuracy decreased along with the drop of speech intelligibility.
Subjects Signal Processing
Keyword(s) Speech
Speech recognition
Feature extraction
Mel frequency cepstral coefficient
Accuracy
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DOI - identifier 10.1109/ICIEA.2013.6566369
Copyright notice © 2013 IEEE
ISBN 9781467363204
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