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Open AccessArticle

Evaluation of Speech Quality Through Recognition and Classification of Phonemes

1
Tomsk State University of Control Systems and Radioelectronics, Tomsk 634050, Russia
2
Tomsk Cancer Research Institute, Tomsk 634050, Russia
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(12), 1447; https://doi.org/10.3390/sym11121447
Received: 24 August 2019 / Revised: 8 November 2019 / Accepted: 20 November 2019 / Published: 25 November 2019
(This article belongs to the Special Issue Information Technologies and Electronics)
This paper discusses an approach for assessing the quality of speech while undergoing speech rehabilitation. One of the main reasons for speech quality decrease during the surgical treatment of vocal tract diseases is the loss of the vocal tractˈs parts and the disruption of its symmetry. In particular, one of the most common oncological diseases of the oral cavity is cancer of the tongue. During surgical treatment, a glossectomy is performed, which leads to the need for speech rehabilitation to eliminate the occurring speech defects, leading to a decrease in speech intelligibility. In this paper, we present an automated approach for conducting the speech quality evaluation. The approach relies on a convolutional neural network (CNN). The main idea of the approach is to train an individual neural network for a patient before having an operation to recognize typical sounding of phonemes for their speech. The neural network will thereby be able to evaluate the similarity between the patientˈs speech before and after the surgery. The recognition based on the full phoneme set and the recognition by groups of phonemes were considered. The correspondence of assessments obtained through the autorecognition approach with those from the human-based approach is shown. The automated approach is principally applicable to defining boundaries between phonemes. The paper shows that iterative training of the neural network and continuous updating of the training dataset gradually improve the ability of the CNN to define boundaries between different phonemes. View Full-Text
Keywords: speech quality; syllable intelligibility; speech recognition; speech rehabilitation; cancer speech quality; syllable intelligibility; speech recognition; speech rehabilitation; cancer
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Pekarskikh, S.; Kostyuchenko, E.; Balatskaya, L. Evaluation of Speech Quality Through Recognition and Classification of Phonemes. Symmetry 2019, 11, 1447.

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