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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prudnikov, A., Korenevsky, M., Aleinik, S.: Adaptive beamforming and adaptive training of DNN acoustic models for enhanced multichannel noisy speech recognition. In: IEEE Automatic Speech Recognition and Understanding Workshop, pp. 401–408. IEEE Press, Scottsdale (2015)
Levin, K., Ponomareva, I., Bulusheva, A., Chernykh, G., Medennikov, I., Merkin, N., Prudnikov, A., Tomashenko, N.: Automated closed captioning for Russian live broadcasting. In: 16th Annual Conference of the International Speech Communication Association (Interspeech), Singapore, pp. 1438–1442 (2014)
Siemund, R., Hoge, H., Kunzmann, S., Marasek, K.: SPEECON speech data for consumer devices. In: Second International Conference on Language Resources and Evaluation, Athens, vol. II, pp. 883–886 (2000)
Arlazarov, V.L., Bogdanov, D.S., Krivnova, O.F., Podrabinovitch, A.Y.: Creation of Russian speech databases: design, processing, development tools. In: SPECOM 2004, Saint-Petersburg, Russia, pp. 650–656 (2004)
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Hannemann, M., Motlicek, P., Qian, Y., Schwarz, P., Silovsky, J., Stemmer, G., Vesely, K.: The kaldi speech recognition toolkit. In: IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 (2011)
Kim, C., Stern, R.: Power-normalized cepstral coefficients (PNCC) for robust speech recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012), pp. 4101–4104 (2012)
Chen, C.-P., Bilmes, J.: MVA processing of speech features. IEEE Trans. Audio, Speech Lang. Process. 15(1), 257–270 (2009)
European Telecommunications Standards Institute, Speech Processing, Transmission and Quality Aspects (STQ); Distributed Speech Recognition; Advanced Front-end Feature Extraction Algorithm; Compression Algorithms, es 202 050, Rev. 1.1.5 edn. (2007)
Viikki, O., Laurila, K.: Cepstral domain segmental feature vector normalization for noise robust speech recognition. Speech Commun. 25, 133–147 (1998)
Mohamed, A., Dahl, G.E., Hinton, G.: Acoustic modeling using deep belief networks. IEEE Trans. Audio, Speech, Lang. Process. 20(1), 14–22 (2012)
Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent deep neural networks for conversational speech transcription. In: 2011 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 24–29. IEEE (2011)
Nesterov, Y.: Introductory Lectures on Convex Optimization. A Basic Course. Kluwer Academic Publishers, New York (2004)
Acknowledgments
This work was financially supported by the Ministry of Education and Science of the Russian Federation, Contract 14.575.21.0033 (ID RFMEFI57514X0033).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-43958-7_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-43957-0
Online ISBN: 978-3-319-43958-7
eBook Packages: Computer ScienceComputer Science (R0)