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2014 Vol.33, Issue 4 Preview Page
Speech Recognition Using MSVQ/TDRNN

MSVQ/TDRNN을 이용한 음성인식

Sung-Suk Kim1*
김 성석1*
1Department of Computer Science, Yong-In University
1용인대학교 컴퓨터과학과
*교신저자. *Corresponding Author. 
31 July 2014. pp. 268-272
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Abstract

This paper presents a method for speech recognition using multi-section vector-quantization (MSVQ) and time-delay recurrent neural network (TDTNN). The MSVQ generates the codebook with normalized uniform sections of voice signal, and the TDRNN performs the speech recognition using the MSVQ codebook. The TDRNN is a time-delay recurrent neural network classifier with two different representations of dynamic context: the time-delayed input nodes represent local dynamic context, while the recursive nodes are able to represent long-term dynamic context of voice signal. The cepstral PLP coefficients were used as speech features. In the speech recognition experiments, the MSVQ/TDRNN speech recognizer shows 97.9 % word recognition rate for speaker independent recognition.

Keywords
Speech recognition
MSVQ
TDRNN

본 논문에서는 MSVQ(Multi-Section Vector Quantization)와 시간지연 회귀 신경회로망(TDRNN)을 이용한 하이브리드 구조의 음성인식 방법을 제안한다. MSVQ는 음성의 길이를 일정한 구간 수로 정규화한 코드북을 생성하고, 시간지연 회귀 신경회로망은 이 코드북을 이용하여 음성을 인식한다. 시간지연 회귀 신경회로망은 음성의 시계열 문맥정보를 잘 학습할 수 있는 구조로 구성되었다. 음성특징으로 인지선형예측(PLP) 계수가 사용되었다. 음성인식 실험을 수행한 결과 MSVQ/TDRNN 음성인식기는 97.9 %의 화자독립 음성 인식률을 보였다.

키워드
음성인식
MSVQ
시간지연 회귀 신경회로망
References
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Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 33
  • No :4
  • Pages :268-272
  • Received Date : 2014-04-16
  • Accepted Date : 2014-05-19
  • DOI :https://doi.org/10.7776/ASK.2014.33.4.268
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