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ARMA Filtering of Speech Features Using Energy Based Weights
반성민,김형순,Ban, Sung-Min,Kim, Hyung-Soon The Acoustical Society of Korea 2012 韓國音響學會誌 Vol.31 No.2
In this paper, a robust feature compensation method to deal with the environmental mismatch is proposed. The proposed method applies energy based weights according to the degree of speech presence to the Mean subtraction, Variance normalization, and ARMA filtering (MVA) processing. The weights are further smoothed by the moving average and maximum filters. The proposed feature compensation algorithm is evaluated on AURORA 2 task and distant talking experiment using the robot platform, and we obtain error rate reduction of 14.4 % and 44.9 % by using the proposed algorithm comparing with MVA processing on AURORA 2 task and distant talking experiment, respectively.
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비만 중년여성의 복합운동이 대사증후군 지표와 건강체력에 미치는 영향
반성민,이경준,양정옥,Ban, Sung-Min,Lee, Kyung-Jun,Yang, Jeong-Ok 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.4
본 연구는 12주간 복합운동프로그램이 비만 중년여성들의 대사증후군 지표와 건강 체력에 미치는 영향을 알아보기 위한 것으로 최근 6개월 이내에 규칙적으로 운동에 참여하지 않는 비만 중년여성 22명을 임의로 선정하였다. 본 연구의 자료분석을 위한 통계프로그램은 SPSS 18.0을 이용하여 모든 종속변인의 평균값과 표준편차를 산출하였으며, 12주 전 후 집단 내 변인들의 변화를 알아보기 위해 윌콕슨의 부호순위 검정을 실시하였고, 각 분석에서의 통계적인 유의수준은 p<.05로 설정하였다. 본 연구에서 실시한 12주간 복합운동프로그램은 비만 중년여성의 대사증후군 지표와 건강체력에 긍정적인 효과를 미친 것으로 나타났다. 따라서 향후 비만 중년여성의 건강관리와 체력향상을 위하여 신체적 여건, 체력수준, 사회 경제적 상황을 충분히 고려한 다양한 수준의 운동프로그램 개발에 노력해야 할 것이다. The purpose of this study is to observe the effects of the 12-week comprehensive exercise program on the metabolic syndrome index and general health of overweight middle aged women. Before and after the exercise program, research participants were measured in metabolic syndrome index and health fitness. The measurements gathered before and after the exercise program were analyzed through SPSS 18.0 to calculate average and standard deviation of all response variables. To find changes in the response variables before and after the 12-week program, Wilcoxon signed rank test was performed at a significance level of ${\alpha}$=.05. The results of this research are as follows. The 12-week comprehensive exercise program has a positive impact on the metabolic index and health fitness of overweight middle-aged women.
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반성민(Ban Sung Min),김형순(Kim Hyung Soon) 한국음성학회 2010 말소리와 음성과학 Vol.2 No.4
In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.
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수정된 MAP 적응 기법을 이용한 음성 데이터 자동 군집화
반성민(Ban, Sung Min),강병옥(Kang, Byung Ok),김형순(Kim, Hyung Soon) 한국음성학회 2014 말소리와 음성과학 Vol.6 No.1
This paper proposes a speaker and environment clustering method in order to overcome the degradation of the speech recognition performance caused by various noise and speaker characteristics. In this paper, instead of using the distance between Gaussian mixture model (GMM) weight vectors as in the Google’s approach, the distance between the adapted mean vectors based on the modified maximum a posteriori (MAP) adaptation is used as a distance measure for vector quantization (VQ) clustering. According to our experiments on the simulation data generated by adding noise to clean speech, the proposed clustering method yields error rate reduction of 10.6% compared with baseline speaker-independent (SI) model, which is slightly better performance than the Google"s approach.
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강인한 음성인식을 위한 극점 필터링 및 스케일 정규화를 이용한 켑스트럼 특징 정규화 방식
최보경,반성민,김형순,Choi, Bo Kyeong,Ban, Sung Min,Kim, Hyung Soon 한국음향학회 2015 韓國音響學會誌 Vol.34 No.4
본 논문에서는 Cepstral Mean Normalization(CMN)과 Cepstral Mean and Variance Normalization(CMVN) 프레임워크에서 극점 필터링(pole filtering) 개념을 Mel-Frequency Cepstral Coefficient(MFCC) 특징 벡터에 적용한다. 또한 분산 정규화를 대신하여 스케일 정규화를 사용하는 Cepstral Mean and Scale Normalization(CMSN)의 성능을 잡음 환경 음성인식 실험을 통해 평가한다. CMN과 CMVN은 보통 발화 단위로 수행되기 때문에 짧은 발화의 경우 특징에 대한 평균과 분산의 추정 신뢰도가 보장되지 않는 문제점을 가지는데, 극점 필터링과 스케일 정규화 방식을 적용함으로 이러한 문제점을 보완할 수 있다. Aurora 2 데이터베이스를 이용한 실험 결과, 극점 필터링과 스케일 정규화를 결합한 특징 정규화 방식의 성능이 가장 높은 성능 향상을 보인다. In this paper, the pole filtering concept is applied to the Mel-frequency cepstral coefficient (MFCC) feature vectors in the conventional cepstral mean normalization (CMN) and cepstral mean and variance normalization (CMVN) frameworks. Additionally, performance of the cepstral mean and scale normalization (CMSN), which uses scale normalization instead of variance normalization, is evaluated in speech recognition experiments in noisy environments. Because CMN and CMVN are usually performed on a per-utterance basis, in case of short utterance, they have a problem that reliable estimation of the mean and variance is not guaranteed. However, by applying the pole filtering and scale normalization techniques to the feature normalization process, this problem can be relieved. Experimental results using Aurora 2 database (DB) show that feature normalization method combining the pole-filtering and scale normalization yields the best improvements.
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화자인식을 위한 주파수 워핑 기반 특징 및 주파수-시간 특징 평가
최영호(Choi, Young Ho),반성민(Ban, Sung Min),김경화(Kim, Kyung-Wha),김형순(Kim, Hyung Soon) 한국음성학회 2015 말소리와 음성과학 Vol.7 No.1
In this paper, different frequency scales in cepstral feature extraction are evaluated for the text-independent speaker recognition. To this end, mel-frequency cepstral coefficients (MFCCs), linear frequency cepstral coefficients (LFCCs), and bilinear warped frequency cepstral coefficients (BWFCCs) are applied to the speaker recognition experiment. In addition, the spectro-temporal features extracted by the cepstral-time matrix (CTM) are examined as an alternative to the delta and delta-delta features. Experiments on the NIST speaker recognition evaluation (SRE) 2004 task are carried out using the Gaussian mixture model-universal background model (GMM-UBM) method and the joint factor analysis (JFA) method, both based on the ALIZE 3.0 toolkit. Experimental results using both the methods show that BWFCC with appropriate warping factor yields better performance than MFCC and LFCC. It is also shown that the feature set including the spectro-temporal information based on the CTM outperforms the conventional feature set including the delta and delta-delta features.
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잡음 환경에서 짧은 발화 인식 성능 향상을 위한 선택적 극점 필터링 기반의 특징 정규화
최보경(Choi, Bo Kyeong),반성민(Ban, Sung Min),김형순(Kim, Hyung Soon) 한국음성학회 2017 말소리와 음성과학 Vol.9 No.2
The pole filtering concept has been successfully applied to cepstral feature normalization techniques for noise-robust speech recognition. In this paper, it is proposed to apply the pole filtering selectively only to the speech intervals, in order to further improve the recognition performance for short utterances in noisy environments. Experimental results on AURORA 2 task with clean-condition training show that the proposed selectively pole-filtered cepstral mean normalization (SPFCMN) and selectively pole-filtered cepstral mean and variance normalization (SPFCMVN) yield error rate reduction of 38.6% and 45.8%, respectively, compared to the baseline system.
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