Abstract
The aim of the paper is to investigate the ways to improve acoustic models for Russian spontaneous speech recognition. We applied the main steps of the Kaldi Switchboard recipe to a Russian dataset but obtained low accuracy with respect to the results for English spontaneous telephone speech. We found two methods to be especially useful for Russian spontaneous speech: the i-vector based deep neural network adaptation and speaker-dependent bottleneck features which provide 8.6 % and 11.9 % relative word error rate reduction over the baseline system respectively.
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Acknowledgements
The work was partially financially supported by the Government of the Russian Federation, Grant 074-U01, and by the Ministry of Education and Science of Russian Federation, contract 14.579.21.0057, ID RFMEFI57914X0057.
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Prudnikov, A., Medennikov, I., Mendelev, V., Korenevsky, M., Khokhlov, Y. (2015). Improving Acoustic Models for Russian Spontaneous Speech Recognition. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_29
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DOI: https://doi.org/10.1007/978-3-319-23132-7_29
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