• The Journal of the Acoustical Society of Korea
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• v.17 no.1
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• pp.4-8
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• 1998

본 논문에서는 다양한 전화선 채널을 통하여 수집된 음성 데이터에 포함된 잡음 및 채널 왜곡을 제거하여 음성인식 시스템의 성능을 향상시키는 방법에 관하여 연구하였다. 전 화선을 통과한 음성에 포함된 채널 잡음 및 왜곡을 제거하는 방법으로는 음성신호를 보상하 는 방법으로 CMS(Cepstral Mean Subtraction), SBR(Signal Bias Removal)과 SM(Stochastic Matching)의 성능을 비교 평가하였다. 잡음제거 방식의 성능을 평가를 위하 여 음소 단위의 반연속 HMM을 이용한 화자독립 단독음 인식을 수행하였다. 인식 실험 결 과, 멜 켑스트럼을 사용한 경우에 CMS가 가장 우수한 성능을 내었고 다음으로 SM과 SBR 순으로 나타났다. 또한 특징벡터를 주변 잡음에 강인하게 하는 가중함수(RPS, BPL)를 사용 한 켑스트럼 계수와 잡음제거 방식을 함께 사용한 경우에 인식 성능이 더욱 향상되었다.

• Speech Sciences
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• v.7 no.3
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• pp.185-195
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• 2000

This paper seeks to compare the feature parameters used in speaker identification systems under noisy environments. The feature parameters compared are LP cepstrum (LPCC), Cepstral mean subtraction(CMS), Pole-filtered CMS(PFCMS), Adaptive component weighted cepstrum(ACW) and Postfilter cepstrum(PF). The GMM-based text independent speaker identification system is designed for this target. Some series of experiments show that the LPCC parameter is adequate for modelling the speaker in the matched environments between train and test stages. But in the mismatched training and testing conditions, modified parameters are preferable the LPCC. Especially CMS and PFCMS parameters are more effective for the microphone mismatching conditions while the ACW and PF parameters are good for more noisy mismatches.

• Speech Sciences
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• v.2
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• pp.57-66
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• 1997

This paper studies the effects of the method, CMS(Cepstral Mean Subtraction), (which compensates for some of the speech distortion. caused by telephone channels), on the performance of the text-dependent speaker identification system. This system is based on the VQ(Vector Quantization) and HMM(Hidden Markov Model) method and chooses the LPC-Cepstrum and Mel-Cepstrum as the feature vectors extracted from the speech data transmitted through telephone channels. Accordingly, we can compare the correct recognition rates of the speaker identification system between the use of LPC-Cepstrum and Mel-Cepstrum. Finally, from the experiment results table, it is found that the Mel-Cepstrum parameter is proven to be superior to the LPC-Cepstrum and that recognition performance improves by about 10% when compensating for telephone channel using the CMS.