Abstract
The article considers an approach to the problem of assessing the quality of speech during speech rehabilitation as a classification problem. For this, a classifier is built on the basis of an LSTM neural network for dividing speech signals into two classes: before the operation and immediately after. At the same time, speech before the operation is the standard to which it is necessary to approach in the process of rehabilitation. The metric of belonging of the evaluated signal to the reference class acts as an assessment of speech. An experimental assessment of rehabilitation sessions and a comparison of the resulting assessments with expert assessments of phrasal intelligibility were carried out.
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Acknowledgements
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. The authors would like to thank the Irkutsk Supercomputer Center of SB RAS for providing access to the HPC-cluster «Akademik V.M. Matrosov» [16].
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Kostyuchenko, E., Rakhmanenko, I., Balatskaya, L. (2022). Assessment of Speech Quality During Speech Rehabilitation Based on the Solution of the Classification Problem. In: Prasanna, S.R.M., Karpov, A., Samudravijaya, K., Agrawal, S.S. (eds) Speech and Computer. SPECOM 2022. Lecture Notes in Computer Science(), vol 13721. Springer, Cham. https://doi.org/10.1007/978-3-031-20980-2_33
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DOI: https://doi.org/10.1007/978-3-031-20980-2_33
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