• The Journal of the Acoustical Society of Korea
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• v.10 no.6
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• pp.5-11
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• 1991

연속음 인식을 위하여 연결된 음향 신호를 음소단위로 분할하는 것이 필요하다. 본 논문에서는 연속 음성에서의 정상상태 구간 검출을 위한 파라미터로서 진폭분포를 이용하는 방법을 제안하였다. 제 안된 진폭분포는 음성신호의 변화특성을 정확히 나타내며 이러한 프레임사이의 진폭분포를 이용하는 방 법을 제안하였다. 제안된 지폭분포는 음성 신호의 변화특성을 정확히 나타내며 이러한 프레임사이의 진 폭 분포 차이값을 비교하여 프레임의 안정구간과 천이구간을 구분할 수 있었다.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.312-323
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• 2014

Voice activity detection is very important process that find voice activity from noisy speech signal for noise cancelling and speech enhancement. Over the past few years, many studies have been made on voice activity detection, it has poor performance for speech signal of sentence form in a low SNR environment. In this paper, it proposed new voice activity detection algorithm that has beginning VAD process using entropy and main VAD process using fuzzy membership shifted c-means clustering. We conduct an experiment in various SNR environment of white noise to evaluate performance of the proposed algorithm and confirmed good performance of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1
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• pp.76-79
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• 1998

소음이 존재하지 않는 환경에서는 에너지 파라메터만으로도 정확한 끝점 검출을 수 행할 수 있으나 신호대 잡음비가 0dB에 가까운 자동차 환경에서는 끝점 검출이 거의 불가 능하다. 본 논문에서는 자동차 소음 환경에서 음성 구간 검출을 위하여 단구간 영교차율과 2∼4kHz의 주파수 영역 에너지를 사용한 끝점 검출 방법을 제안하였다. 제안된 방법과 기 존의 방법의 성능을 DTW를 이용한 단독음 인식 시스템에 적용하여 인식률로 비교하였으 며 제안된 음성 구간 검출 방법을 적용한 경우가 보다 좋은 인식률을 나타내었다.

• The Journal of the Acoustical Society of Korea
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• v.34 no.4
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• pp.321-327
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• 2015

In this paper, we propose an algorithm for achieving robust Visual Voice Activity Detection (VVAD) for enhanced speech recognition. In conventional VVAD algorithms, the motion of lip region is found by applying an optical flow or Chaos inspired measures for detecting visual speech frames. The optical flow-based VVAD is difficult to be adopted to driving scenarios due to its computational complexity. While invariant to illumination changes, Chaos theory based VVAD method is sensitive to motion translations caused by driver's head movements. The proposed Local Variance Histogram (LVH) is robust to the pixel intensity changes from both illumination change and translation change. Hence, for improved performance in environmental changes, we adopt the novel threshold estimation using total variance change. In the experimental results, the proposed VVAD algorithm achieves robustness in various driving situations.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.519-528
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• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.169-174
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• 2010

This paper explains end-point detection method for better speech recognition rates. The proposed method determines speech and non-speech region with the entropy and the harmonic detection of speech. The end-point detection using entropy on the speech spectral energy has good performance at the high SNR(SNR 15dB) environments. At the low SNR environment(SNR 0dB), however, the threshold level of speech and noise varies, so the precise end-point detection is difficult. Therefore, this paper introduces the end-point detection methods which uses speech spectral entropy and harmonics. Experiment shows better performance than the conventional entropy methods.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.391-394
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• 1998

본 논문에서는 극심한 가산 잡음에 의해 손상된 음성 신호를 스펙트럼 차감법으로 개선할 때, 잡음 스펙트럼 추정을 위한 무음 구간 추정 방법을 제안한다. 스펙트럼 차감법은 잡음을 효과적으로 제거한다고 알려져 있으나, SNR 0 dB 이하의 잡음 환경에서는 무음 구간의 검출이 힘들어 잡음 스펙트럼 추정치의 정확도가 저하된다. 일반화 스펙트럼 차감법의 과차감(oversubtraction)과 잡음 스펙트럼 추정을 반복하여 얻은 무음 구간은 SNR -10 dB~ 0 dB의 낮은 SNR에서도 비교적 정확하며, 프레임 에너지를 이용한 무음 검출 방법에 비해 향상된 성능을 보였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.101-107
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• 2011

The background noises and distortions by channel are major factors that disturb the practical use of speech recognition. Usually, noise reduce the performance of speech recognition system DSR(Distributed Speech Recognition) based speech recognition also bas difficulty of improving performance for this reason. Therefore, to improve DSR-based speech recognition under noisy environment, this paper proposes a method which detects accurate speech region to extract accurate features. The proposed method distinguish speech and noise by using entropy and detection of spectral energy of speech. The speech detection by the spectral energy of speech shows good performance under relatively high SNR(SNR 15dB). But when the noise environment varies, the threshold between speech and noise also varies, and speech detection performance reduces under low SNR(SNR 0dB) environment. The proposed method uses the spectral entropy and harmonics of speech for better speech detection. Also, the performance of AFE is increased by precise speech detections. According to the result of experiment, the proposed method shows better recognition performance under noise environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.4
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• pp.991-998
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• 2010

In realization of wireless home network system using speech recognition in indoor voice recognition environment, background noise and reverberation are two main causes of digression in voice recognition system. In this study, the home network system resistant to reverberation and background noise using voice section detection method based on spectral entropy in indoor recognition environment is to be realized. Spectral subtraction can reduce the effect of reverberation and remove noise independent from voice signal by eliminating signal distorted by reverberation in spectrum. For effective spectral subtraction, the correct separation of voice section and silent section should be accompanied and for this, improvement of performance needs to be done, applying to voice section detection method based on entropy. In this study, experimental and indoor environment testing is carried out to figure out command recognition rate in indoor recognition environment. The test result shows that command recognition rate improved in static environment and reverberant room condition, using voice section detection method based on spectral entropy.

• Proceedings of the Acoustical Society of Korea Conference
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• 1995.06a
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• pp.216-220
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• 1995

음성 신호처리분야의 기반을 이루고 잇는 선형예측기법으로 성대폐쇄구간 분석이 가능해질 경우 특히 음성합성가 부호화 시스템의 상당한 성능개선을 기대할 수 있기 때문에 최근 관련 분야에서 높은 신뢰도를 갖는 GCI 검출 알고리즘 개발에 많은 관심을 보이고 있다. 성대폐쇄구간 검출에서 가장 중요한 것은 성대폐쇄시점에 관한 정보이며, 본 논문에서는 이에 대응될 수 있는 정보인 epoch를 음성신호에서 직접 추출할 수 있는 기법을 제안하였다. 제안된 방법은 프레임 단위별 평균 피치를 참조하여 저역통과된 유성음 신호에서 3구간 영교차점별 평균진폭 변동율에 의해 pseudo-epoch를 검출한다. 대역 통과된 유성음 신호를 이용하여 pseudo-epoch 부근에 존재하는 보다 정밀한 실제 epoch을 최종적으로 결정하였다. 제안된 방법은 단계적으로 epoch가 존재할 수 있는 연역을 좁혀 나아가면서 처리하므로 검출오차를 줄일 수 있었고, 시간영역에서 처리되어 계산량이 적으므로 고속 처리가 가능하였다. 성능평가를 위해 처리결과를 EGG 신호와 비교한 결과 약 2샘플 정도의 오차만을 갖는 우수한 성능을 나타내었다.