Abstract
Tracheoesophageal (TE) speech is a possibility to restore the ability to speak after laryngectomy, i.e. the removal of the larynx. TE speech often shows low audibility and intelligibility which also makes it a challenge to automatic speech recognition. We improved the recognition results by adapting a speech recognizer trained on normal, non-pathologic voices to single TE speakers by unsupervised HMM interpolation.
In speech rehabilitation the patient’s voice quality has to be evaluated. As no objective classification means exists until now and an automation of this procedure is desirable we performed initial experiments for automatic evaluation of the intelligibility. We compared scoring results for TE speech from five experienced raters with the word accuracy from different types of speech recognizers. Correlation coefficients of about –0.8 are promising for future work.
This work was partly funded by the EU in the project PF-STAR under grant IST-2001-37599. The responsibility for the contents of this study lies with the authors.
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Haderlein, T., Steidl, S., Nöth, E., Rosanowski, F., Schuster, M. (2004). Automatic Recognition and Evaluation of Tracheoesophageal Speech. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_42
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DOI: https://doi.org/10.1007/978-3-540-30120-2_42
Publisher Name: Springer, Berlin, Heidelberg
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