• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.57-63
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• 1997

In this paper, we study the efficient feature vector extraction method and front-end processing to improve the performance of the speech recognition system using KT(Korea Telecommunication) database collected through various telephone channels. First of all, we compare the recognition performances of the feature vectors known to be robust to noise and environmental variation and verify the performance enhancement of the recognition system using weighted cepstral distance measure methods. The experiment result shows that the recognition rate is increasedby using both PLP(Perceptual Linear Prediction) and MFCC(Mel Frequency Cepstral Coefficient) in comparison with LPC cepstrum used in KT recognition system. In cepstral distance measure, the weighted cepstral distance measure functions such as RPS(Root Power Sums) and BPL(Band-Pass Lifter) help the recognition enhancement. The application of the spectral subtraction method decrease the recognition rate because of the effect of distortion. However, RASTA(RelAtive SpecTrAl) processing, CMS(Cepstral Mean Subtraction) and SBR(Signal Bias Removal) enhance the recognition performance. Especially, the CMS method is simple but shows high recognition enhancement. Finally, the performances of the modified methods for the real-time implementation of CMS are compared and the improved method is suggested to prevent the performance degradation.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1291-1295
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• 2004

This paper proposed a new approach for endpoint detection of isolated speech, which proves to significantly improve the endpoint detection performance. The proposed algorithm relies on the root mean square energy (rms energy), zero crossing rate and spectral characteristics of the speech signal where the Euclidean distance measure is adopted using cepstral coefficients to accurately detect the endpoint of isolated speech. The algorithm offers better performance than traditional energy-based algorithm. The vocabulary for the experiment includes English digit from one to nine. These experimental results were conducted by 360 utterances from a male speaker. Experimental results show that the accuracy of the algorithm is quite acceptable. Moreover, the computation overload of this algorithm is low since the cepstral coefficients parameters will be used in feature extraction later of speech recognition procedure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.12B
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• pp.2046-2051
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• 2000

본 논문에서는 기존의 음질 평가 방법들보다 우수할 뿐 아니라 다양한 채널 경로의 음성 신호에 대해서도 일관된 성능을 갖는 새로운 음질 평가 방법 PLP-CMS(Perceptual Linear Predictive-Cepstral Mean Subtraction)를 제안한다. CDMA PCS 이동 전화 환경에서 음성 신호의 주관적 음질을 효과적으로 예측할 수 있는 PLP-CMS는 심리 음향 선형 예측 분석(PLP Analysis: Perceptual Linear Predictive Analysis)을 이용하여 주관적 음질과의 상관 관계를 높였으며, 겝스트럼 평균 차감(CMS: Cepstral Mean Subtraction) 과정을 통하여 PSTN 경로에 무관하게 일관된 성능을 갖음을 확인하였다.

• MALSORI
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• no.60
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• pp.191-201
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• 2006

Discrimination between speech and music is important in many multimedia applications. Previously we proposed a new parameter for speech/music discrimination, the mean of minimum cepstral distances (MMCD), and it outperformed the conventional parameters. One weakness of MMCD is that its performance depends on range of candidate frames to compute the minimum cepstral distance, which requires the optimal selection of the range experimentally. In this paper, to alleviate the problem, we propose a multi-dimensional MMCD parameter which consists of multiple MMCDS with combination of different candidate frame ranges. Experimental results show that the multi-dimensional MMCD parameter yields an error rate reduction of 22.5% compared with the optimally chosen one-dimensional MMCD parameter.

• Speech Sciences
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• v.3
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• pp.167-176
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• 1998

Conventional time-scale modification methods have the problem that as the modification rate gets higher the time-scale modified speech signal becomes less intelligible, because they ignore the effect of articulation rate on speech characteristics. Results of research on speech perception show that the timing information of transient portions of a speech signal plays an important role in discriminating among different speech sounds. Inspired by this fact, we propose a novel scheme for modifying the time-scale of speech. In the proposed scheme, the timing information of the transient portions of speech is preserved, while the steady portions of speech are compressed or expanded somewhat excessively for maintaining overall time-scale change. In order to identify the transient and steady portions of a speech signal, we employ a simple method using LPC cepstral distance between neighboring frames. The result of the subjective preference test indicates that the proposed method produces performance superior to that of the conventional SOLA method, especially for very fast playback case.

• Korean Journal of Optics and Photonics
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• v.15 no.2
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• pp.123-129
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• 2004

This paper concerns auto vergence control of a parallel stereoscopic camera through geometrical analysis. In the construction of a parallel stereoscopic camera, we experimentally demonstrated linear relationship between the key object distance and the amount of vergence control. And we proposed a vergence control system for the stereoscopic camera using binocular disparity information. For the real-time calculation of disparity information, the Hybrid Cepstral filter algorithm, with input data acquired from the vertical projection data and from the down sampling data from the source images, was proposed for precision and high speed processing. With the disparity information algorithm and the vergence control of the parallel stereoscopic camera system, the stereoscopic images become more like those of the human eye.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.217-223
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• 2005

In this paper, we propose a predictive block constrained trellis coded quantization (BC-TCQ) to quantize cepstral coefficients for the distributed speech recognition. For Prediction of the cepstral coefficients. the 1st order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively. we use a BC-TCQ. The performance is compared to the split vector quantizers used in the ETSI standard, demonstrating reduction in the cepstral distance and computational complexity.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.2
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• pp.126-134
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• 1990

In this study, eight function discrimination and recognition of the EMG signal from the biceps and triceps of 4 subjects were executed, using the Euclidean and weighted cepstral distance measure with LPC cepstrum coefficients. In case of Euclidean cepstral distance measure, as the number of LPC cepstrum coefficients was increased in 8, 10, 12, 14 the recognition rates of functions are 94.69, 95.63, 96.56, and 96.88[%], respectively, but increasing rates of recognition were inclined to decrease. In case of weighted cepstral distance measure, when the number of LPC cepstrum coefficients was 8, 10, 12 and 14, the recognition rates of functions were 91.88, 95, 99.69, and 96.63[%], respectively.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.174-182
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• 1996

In continuous speech recognition system, it is possible to implement the system which can handle unlimited number of words by using limited number of phonetic units such as phonemes. Dividing continuous speech into the string of tems of phonemes prior to recognition process can lower the complexity of the system. But because of the coarticulations between neiboring phonemes, it is very difficult ot extract exactly their boundaries. In this paper, we propose the algorithm ot extract short terms which can represent each phonemes instead of extracting their boundaries. The short terms of lower spectral change and higher spectral chang eare detcted. Then phoneme changes are detected using distance measure with this lower spectral change terms, and hgher spectral change terms are regarded as transition terms or short phoneme terms. Finally lower spectral change terms and the mid-term of higher spectral change terms are regarded s the represent each phonemes. The cepstral coefficients and weighted cepstral distance are used for speech feature and measuring the distance because of less computational complexity, and the speech data used in this experimetn was recoreded at silent and ordinary in-dorr environment. Through the experimental results, the proposed algorithm showed higher performance with less computational complexity comparing with the conventional segmetnation algorithms and it can be applied usefully in phoneme-based continuous speech recognition.

• MALSORI
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• no.64
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• pp.155-169
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• 2007

Discrimination between speech and music is important in many multimedia applications. In our previous work, focusing on the spectral change characteristics of speech and music, we presented a method using the mean of minimum cepstral distances (MMCD), and it showed a very high discrimination performance. In this paper, to further improve the performance, we propose to employ time-averaged MFCC in computing the MMCD. Our experimental results show that the proposed method enhances the discrimination between speech and music. Moreover, the proposed method overcomes the weakness of the conventional MMCD method whose performance is relatively sensitive to the choice of the frame interval to compute the MMCD.