• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.348-355
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• 2010

In spite of development in speech recognition technology, speech recognition under noisy environment is still a difficult task. To solve this problem, Researchers has been proposed different methods where they have been used visual information except audio information for visual speech recognition. However, visual information also has visual noises as well as the noises of audio information, and this visual noises cause degradation in visual speech recognition. Therefore, it is one the field of interest how to extract visual features parameter for enhancing visual speech recognition performance. In this paper, we propose a method for visual feature parameter extraction based on image-base approach for enhancing recognition performance of the HMM based visual speech recognizer. For experiments, we have constructed Audio-visual database which is consisted with 105 speackers and each speaker has uttered 62 words. We have applied histogram matching, lip folding, RASTA filtering, Liner Mask, DCT and PCA. The experimental results show that the recognition performance of our proposed method enhanced at about 21% than the baseline method.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.45-48
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• 2001

립리딩은 잡음 환경 하에서 음성 인식 성능을 향상을 위해 영상정보를 이용한 바이모달(bimodal)음성인식으로 연구되었다[1][2]. 그 일환으로 이미 영상정보를 이용한 립리딩은 구현되었다. 그러나 현재까지의 시스템들은 환경의 변화에 강인하지 못하다. 본 논문에서는 이미지 기반 립리딩 방법을 적용하여 입술 영역을 보다 안정적으로 찾아 성능을 향상 시켰다. 그러나 이 방법은 많은 데이터량을 처리해야 하므로 전처리 과정이 필요하다. 전처리로 입력영상을 그레이 레벨로 변환하는 방법과, 입술을 반으로 접는 방법, 그리고 주성분 분석(PCA: Principal Component Analysis)을 사용하였다. 또한 인식성능 향상을 위해 음성에서 잡음 제거나 분석$\cdot$합성에 효과적인 성능을 보이는 RASTA(Relative Spectral)필터를 적용하여 시간 영역에서의 변화가 적은 성분이나 급변하는 성분, 그 밖의 잡음 등을 제거하였다. 그 결과 $72.7\%$의 높은 인식 성능을 보였다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.12-20
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• 1999

In this paper, we compared speaker verification performance of the speech data collected in clean environment and in channel environment. For the improvement of the performance of speaker verification gathered in channel, we have studied on the efficient feature parameters in channel environment and on the preprocessing. Speech DB for experiment is consisted of Korean doublet of numbers, considering the text-prompted system. Speech features including LPCC(Linear Predictive Cepstral Coefficient), MFCC(Mel Frequency Cepstral Coefficient), PLP(Perceptually Linear Prediction), LSP(Line Spectrum Pair) are analyzed. Also, the preprocessing of filtering to remove channel noise is studied. To remove or compensate for the channel effect from the extracted features, cepstral weighting, CMS(Cepstral Mean Subtraction), RASTA(RelAtive SpecTrAl) are applied. Also by presenting the speech recognition performance on each features and the processing, we compared speech recognition performance and speaker verification performance. For the evaluation of the applied speech features and processing methods, HTK(HMM Tool Kit) 2.0 is used. Giving different threshold according to male or female speaker, we compare EER(Equal Error Rate) on the clean speech data and channel data. Our simulation results show that, removing low band and high band channel noise by applying band pass filter(150～3800Hz) in preprocessing procedure, and extracting MFCC from the filtered speech, the best speaker verification performance was achieved from the view point of EER measurement.