Abstract
In this paper we consider the problem of the DNN-HMM acoustic models training for automatic speech recognition systems on russian language in modern commercial trucks. The speech database for training and testing the ASR system was recorded in various models of trucks, operating under different conditions. The experiments on the test part of the speech database, show that acoustic models trained on the base of specifically modeled training speech database enable to improve the recognition quality in a moving truck from 35 % to 88 % compared to the acoustic models trained on a clean speech. Also a new topology of the neural network was proposed. It allows to reduce the computational costs significantly without loss of the recognition accuracy.
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Acknowledgments
This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.575.21.0033 (ID RFMEFI57514X0033).
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Korenevsky, M., Medennikov, I., Shchemelinin, V. (2016). Improving the Quality of Automatic Speech Recognition in Trucks. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_43
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DOI: https://doi.org/10.1007/978-3-319-43958-7_43
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