Improving the efficiency of multi-channel voice recognition system

Authors

  • Александр Анатольевич Штепа State Higher Education Establishment “Donetsk National Technical University”, Shybankova Square, 2, Krasnoarmiysk, Donetsk region, 85300, Ukraine https://orcid.org/0000-0002-3860-4331

DOI:

https://doi.org/10.15587/2312-8372.2015.51796

Keywords:

voice recognition, adaptive compensation, proximity measure

Abstract

The use of methods of noisy signal treatment in voice recognition is discussed and some of the research results in this area are given. The main aim of the study is improving the effectiveness of recognition system of voice commands in a difficult acoustic environment by improving the signal / noise ratio due to the use of the spatial separation of signals using multiple directional microphones and digital signal processing on the basis of adaptive interference cancellation. The use of modern methods of language voice recognition together with the adaptive compensation method for processing a noisy signal can improve the accuracy of voice recognition. The algorithmic and structural approaches to solving the problems of sound processing, resulting in difficult acoustic conditions for further recognition of voice commands are discussed in the article. The presented method allows increasing the accuracy of the definition of basic and auxiliary channels of multi-channel voice recognition system to increase the efficiency of adaptive compensation. The method and the algorithm are designed to automatically determine the most appropriate channel for base and support channels in accordance with the method of adaptive interference cancellation. We propose to use proximity measure between the received signal and the standard obtained at command recognition on the basis of non-linear time alignment as a criterion for determining the base channel. The research results can be applied to voice recognition in the voice control systems of equipment and vehicles.

Author Biography

Александр Анатольевич Штепа, State Higher Education Establishment “Donetsk National Technical University”, Shybankova Square, 2, Krasnoarmiysk, Donetsk region, 85300

Candidate of Technical Science

Department of Electronic Engineering

References

  1. Chuchupal, V. Ya., Chichagov, A. S., Makovkin, K. A.; In: Zhuravlev, Yu. I. (1998). Tsifrovaia fil'tratsiia zashumlennyh rechevyh signalov. Soobshchenie po programmnomu obespecheniiu EVM. Moscow: Vychislitel'nyi tsentr RAN, 52.
  2. McWhirter, J., Palmer, K., Roberts, J. (1982). A digital adaptive noise-canceller based on a stabilized version of the widrow L.M.S. algorithm. ICASSP ’82. IEEE International Conference on Acoustics, Speech, and Signal Processing. Institute of Electrical & Electronics Engineers (IEEE), 1394–1397. doi:10.1109/icassp.1982.1171457
  3. Sondhi, M. M., Schmidt, C. E., Rabiner, L. R. (1981, October). Improving the Quality of a Noisy Speech Signal. Bell System Technical Journal, Vol. 60, № 8, 1847–1859. doi:10.1002/j.1538-7305.1981.tb00299.x
  4. Hoy, L., Burns, B., Soldan, D., Yarlagadda, R. (1983). Noise suppression methods for speech applications. ICASSP ’83. IEEE International Conference on Acoustics, Speech, and Signal Processing. Institute of Electrical & Electronics Engineers (IEEE), 1133–1136. doi:10.1109/icassp.1983.1171985
  5. Ricketts, T., Dhar, S. (1999). Comparison of performance across three directional hearing aids. Journal of the American Academy of Audiology, Vol. 10, № 4, 180–189.
  6. Gnewikow, D., Ricketts, T., Bratt, G. W., Mutchler, L. C. (2009). Real-world benefit from directional microphone hearing aids. Journal of Rehabilitation Research & Development, Vol. 46, № 5, 603–618. doi:10.1682/jrrd.2007.03.0052
  7. Nyffeler, M. (2010). Auto ZoomControl – Automatic change of focus to speech signals of interest. Field Study News. Available: https://www.phonakpro.com/content/dam/phonakpro/gc_hq/en/resources/evidence/field_studies/documents/fsn_2010_September_AutoZoomControl.pdf
  8. Nyffeler, M., Dechant, S. (2008). Field Study on User Control of Directional Focus: Benefits of Hearing the Facets of a Full Life. Hearing Review, Vol. 16, № 1, 24–28.
  9. Widrow, B., Glover, J. R., McCool, J. M., Kaunitz, J., Williams, C. S. et al. (1975). Adaptive noise cancelling: Principles and applications. Proceedings of the IEEE, Vol. 63, № 12, 1692–1716. doi:10.1109/proc.1975.10036
  10. Gladyshev, K. K. (2010). Informativnye priznaki na osnove lineinyh spektral'nyh kornei v sistemah raspoznavaniia rechevyh komand. St. Petersburg: St. Petersburg State University of Telecommunications n.a. prof. Bonch-Bruevich, 16.

Published

2015-09-22

How to Cite

Штепа, А. А. (2015). Improving the efficiency of multi-channel voice recognition system. Technology Audit and Production Reserves, 5(2(25), 26–31. https://doi.org/10.15587/2312-8372.2015.51796

Issue

Section

Information Technologies: Original Research