• ETRI Journal
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• v.30 no.5
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• pp.753-755
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• 2008

In this letter, we propose a new histogram equalization technique for feature compensation in speech recognition under noisy environments. The proposed approach combines a signal-to-noise-ratio-dependent feature reconstruction method and the class histogram equalization technique to effectively reduce the acoustic mismatch present in noisy speech features. Experimental results from the Aurora 2 task confirm the superiority of the proposed approach for acoustic feature compensation.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.43-48
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• 2003

The effects of linear acoustic channels have been analyzed and compensated at mel-frequency feature domain. Unlike popular RASTA filtering our approach incorporates separate filters for each mel-frequency band, which results in better recognition performance for heavy-reverberated speeches.

• The Journal of the Acoustical Society of Korea
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• v.18 no.5
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• pp.58-62
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• 1999

This paper proposes use of acoustic parameters to improve the discriminability among digit models in Korean connected digit recognition. The proposed method used the logarithmic values of energy ratio between the predetermined frequency bands as additional feature parameters, based on the acoustic-phonetic knowledge. The results of our experiment show that the proposed method reduced the error rate by 46% in comparison with the baseline system. And incorporation of channel compensation technique in the proposed method yielded error reduction of about 69%.

• ETRI Journal
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• v.28 no.4
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• pp.502-505
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• 2006

A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating the acoustic mismatch between training and test environments, but also at reducing the discrepancy between the phonetic distributions of training and test speech data. The algorithm utilizes multiple class-specific reference and test cumulative distribution functions, classifies the noisy test features into their corresponding classes, and equalizes the features by using their corresponding class-specific reference and test distributions. Experiments on the Aurora 2 database proved the effectiveness of the proposed method by reducing relative errors by 18.74%, 17.52%, and 23.45% over the conventional histogram equalization method and by 59.43%, 66.00%, and 50.50% over mel-cepstral-based features for test sets A, B, and C, respectively.

• MALSORI
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• no.60
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• pp.145-164
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• 2006

This paper proposes class histogram equalization (CHEQ) to compensate noisy acoustic features for robust speech recognition. CHEQ aims to compensate for the acoustic mismatch between training and test speech recognition environments as well as to reduce the limitations of the conventional histogram equalization (HEQ). In contrast to HEQ, CHEQ adopts multiple class-specific distribution functions for training and test environments and equalizes the features by using their class-specific training and test distributions. According to the class-information extraction methods, CHEQ is further classified into two forms such as hard-CHEQ based on vector quantization and soft-CHEQ using the Gaussian mixture model. Experiments on the Aurora 2 database confirmed the effectiveness of CHEQ by producing a relative word error reduction of 61.17% over the baseline met-cepstral features and that of 19.62% over the conventional HEQ.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.11
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• pp.93-100
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• 2005

In this paper, we implement the embedded speech recognition system to support various application services such as audio and video control using speech recognition interface on cars. The embedded speech recognition system is implemented and ported in a DSP board. Because MIC type and speech codecs affect the accuracy of speech recognition. And also, we optimize the simulation and test environment to effectively remove the real noises on a car. We applied a noise suppression and feature compensation algorithm to increase an accuracy of sppech recognition on a car. And we used a context dependent tied-mixture acoustic modeling. The performance evaluation showed high accuracy of proposed system in office environment and even real car environment.