Browse > Article

Effective Feature Extraction in the Individual frequency Sub-bands for Speech Recognition  

지상문 (경성대학교 컴퓨터과학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.7, no.4, 2003 , pp. 598-603 More about this Journal
Abstract
This paper presents a sub-band feature extraction approach in which the feature extraction method in the individual frequency sub-bands is determined in terms of speech recognition accuracy. As in the multi-band paradigm, features are extracted independently in frequency sub-regions of the speech signal. Since the spectral shape is well structured in the low frequency region, the all pole model is effective for feature extraction. But, in the high frequency region, the nonparametric transform, discrete cosine transform is effective for the extraction of cepstrum. Using the sub-band specific feature extraction method, the linguistic information in the individual frequency sub-bands can be extracted effectively for automatic speech recognition. The validity of the proposed method is shown by comparing the results of speech recognition experiments for our method with those obtained using a full-band feature extraction method.
Keywords
부대역 특징추출;전대역 특징추출;다중대역 음성인식;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 H. Bourland and S. Dupont. 'ASR based on independent processing and recombination of partial frequency bands,' Proc. Int. Com. on Spoken Language Processing, 1. 422-425, 1996
2 H. Hermansky, 'Perceptual linear predictive (PLP) analysis of speech,' J. Acoust. Soc. Am. 87 (4), 1738-1752, April 1990   DOI   PUBMED
3 지상문, 조훈영, 오영환, '주파수 부대역의 켑스트럼 해상도 최적화에 의한 특징추출,' 한국음향학회지, 제 22권 제 1호, 2003
4 Y. C. Tam and B. Mak, 'Optimization of sub-band weights using simulated noisy speech in multi-band speech recognition,' Proc, Int. Conf. on Spoken Language Processing, 2000
5 J. B. Allen, 'How do humans process and recognize speech?,' IEEE Trans. On Speech and Audio Processing, 2 (4), 567-577, October 1994   DOI   ScienceOn
6 조훈영, 지상문, 오영환, '다중대역 음성인식을 위한 부대역 신뢰도의 추정 및 가중.' 한국음향학회지, 제 21권 제 6호, 2002
7 R. G. Reonard, 'A database for speaker-independent digit recognition,' Proc. ICASSP, 3, 42.11/1-4, 1984
8 H. N. Mirghafori, 'A multi-band approach to automatic speech recognition,' lCI TR-99-04, 1999
9 H. Hermansky and N. Morgan, 'RASTA Processing of speech,' IEEE Trans. On Speech and Audio Processing, 2 (4), 578-589, October 1994   DOI   ScienceOn
10 H. Hermansky, S. Tibrewala and M. Pavel, 'Towards ASR on partially corrupted speech,' Proc. Int. Conf. on Spoken Language Processing, 1. 462-465, 1996
11 C. Cerisara and D. Fohr, 'Multi-band automatic speech recognition,' Computer Speech and Language, 15, 151-174, 2001   DOI   ScienceOn
12 S. Okawa, T. Nakajima and K. Shirai, 'A recombination strategy for multi-band speech recognition based on mutual information criterion,' Proc. EUROSPEECH, 2, 603-606, 1999
13 C. Christophe, H. J. Paul and F. Dominique, 'Towards a global optimization scheme for multi-band speech recognition,' Proc. EUROSPEECH, 2, 587-590, 1999