Abstract
The article considers an algorithm for segmenting a speech signal into sections of silence and voiced and unvoiced segments. The use of the segmentation algorithm will make it possible to assess the quality of speech based on the comparison of two different implementations of the same phoneme separately, not as part of a whole syllable. The algorithm under study is based on the classification of individual signal frames into classes of silence, unvoiced or voiced section and the selection of segments by combining adjacent frames of the same classes. Testing showed the efficiency of the algorithm both on normal (undistorted) speech and on distorted speech of patients undergoing speech rehabilitation. An algorithm was investigated with class definition parameters proposed by the authors of the algorithm. Also, new classification parameters were proposed, selected using methods for optimizing the values of the exposed segment boundaries based on the analysis of a dataset from normal and distorted speech. The use of optimally selected parameters made it possible to reduce the segmentation error by an average of 60%. The applicability of the considered segmentation algorithm for solving the problem of dividing syllables into phonemes is shown.
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Acknowledgments
Acknowledgments. This research was funded by the Ministry of Science and Higher Education of the Russian Federation within the framework of scientific projects carried out by teams of research laboratories of educational institutions of higher education subordinate to the Ministry of Science and Higher Education of the Russian Federation, project number FEWM-2020–0042.
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Novokhrestova, D., Kostyuchenko, E., Krivoshein, I., Balatskaya, L. (2023). Speech Signal Segmentation into Silence, Unvoiced and Vocalized Sections in Speech Rehabilitation. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14338. Springer, Cham. https://doi.org/10.1007/978-3-031-48309-7_48
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