Implementation of spoken words neural network recognition on a mobile robotic platform based on signal processors

M.A. Yakovlev; P.V. Skribtsov; M.A. Chervonenkis; S.N. Zagoruyko

Implementation of spoken words neural network recognition on a mobile robotic platform based on signal processors

M.A. Yakovlev¹, P.V. Skribtsov², M.A. Chervonenkis³, S.N. Zagoruyko⁴

research article Pages: 21-25

^1,4 программист-математик, ООО «ПАВЛИН Техно»
² к.т.н., ген. директор, ООО «ПАВЛИН Техно»
³ вед. разработчик, ООО «ПАВЛИН Техно»

Keywords: isolatedwordrecognition neural networks robotics mel frequency cepstral coefficients speaker-independent speech recognition noise compensation robotic platform signal processors

Abstract

Addressed in this paper are the principals of word recognition system implementation on a mobile robotic platform. Two classification algorithms used for recognition were studied: an algorithm based on k-nearest neighbors method and neural network algorithm. Article describes common approaches to implementation as well as peculiarities of design which are required for a system to operate on a robotic platform.

How to cite

Яковлев М.А., Скрибцов П.В., Червоненкис М.А., Загоруйко С.Н. Особенности реализации распознавания произносимых слов с помощью нейронных сетей для мобильной роботизированной платформы с применением сигнальных процессоров // Нейрокомпьютеры: разработка, применение. 2012. № 10. С. 21–25.

References

Официальный сайт проекта CMUSphinxhttp://cmusphinx.sourceforge.net/
Официальный сайт проекта Juliushttp://julius.sourceforge.jp/en_index.php/
Lamel, L. F., Rabiner, L. R., Rosenberg, A. E., Wilpong, J. G., An improved endpoint detection for isolated word recognition // IEEE Transactions on acoustics, speech, and signal processing. AUGUST 1981. V. ASSP-29. № 4.
Описание стандарта ETSI201 108http://www.etsi.org/deliver/etsi_es/201100_201199/201108/01.01.03_60/es_201108v010103p.pdf
Ratanamahatana, C. A.,Keogh, E., Three Myths about Dynamic Time Warping Data Mining // Department of Computer Science and Engineering University of California, Riverside.
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[1] Официальный сайт проекта CMUSphinxhttp://cmusphinx.sourceforge.net/

[2] Официальный сайт проекта Juliushttp://julius.sourceforge.jp/en_index.php/

[3] Lamel, L. F., Rabiner, L. R., Rosenberg, A. E., Wilpong, J. G., An improved endpoint detection for isolated word recognition // IEEE Transactions on acoustics, speech, and signal processing. AUGUST 1981. V. ASSP-29. № 4.

[4] Описание стандарта ETSI201 108http://www.etsi.org/deliver/etsi_es/201100_201199/201108/01.01.03_60/es_201108v010103p.pdf

[5] Ratanamahatana, C. A.,Keogh, E., Three Myths about Dynamic Time Warping Data Mining // Department of Computer Science and Engineering University of California, Riverside.

[6] Choi, E. H. C., Noise Robust Front-end for ASR using Spectral Subtraction, Spectral Flooring and Cumulative Distribution Mapping - Multimodal User Interaction National ICT Australia.

[7] Vinyals, O., Ravuri, S. V., Povey, D., Revisiting recurrent neural networks for robust ASR // International Computer Science Institute, Berkeley. CA. USA EECS Department, University of California. Berkeley. Berkeley. CA, USAMicrosoft Research, Redmond. WA.USA.

Abstract

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