• The Journal of the Acoustical Society of Korea
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• v.31 no.3
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• pp.161-169
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• 2012

This paper presents a robust speech end-point detector using double talk detection in echoic conditioned speech recognition robot. The proposed method consists of combining conventional end-point detector result and double talk detector result. We have tested the proposed method in isolated word recognition system under echoic conditioned environment. As a result, the proposed algorithm shows superior performance of 30 % to the available techniques in the points of speech recognition rates.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.5
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• pp.289-297
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• 2003

The performance of a bimodal system is affected by the accuracy of the endpoint detection from the input signal as well as the performance of the speech recognition or lipreading system. In this paper, we propose the endpoint detection method which detects the endpoints from the audio and video signal respectively and utilizes the signal to-noise ratio (SNR) estimated from the input audio signal to select the reliable endpoints to the acoustic noise. In other words, the endpoints are detected from the audio signal under the high SNR and from the video signal under the low SNR. Experimental results show that the bimodal system using the proposed endpoint detector achieves satisfactory recognition rates, especially when the acoustic environment is quite noisy.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1718-1720
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• 2013

본 논문에서는 다중 손 끝점 검출을 위해 특징 추출 기법 및 이를 기반으로 한 손 끝점 검출 알고리즘을 제안한다. 특징 추출을 위해 Local Binary Feature(LBP)을 사용하였고 특징의 차원을 축소하기 위해 Principal Component Analysis(PCA) 기법을 이용하였다. 손 끝점 판별을 위해 Reduced multivariate polynomial Model(RM) Classifier를 사용하여 실험 결과 제안된 손 끝점 검출 기법이 다양한 환경에서 동작 하는 것을 확인 하였다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.6
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• pp.57-64
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• 1999

In this paper, we investigated the robust endpoint detection algorithm in noisy environment. A new feature parameter based on a discrete wavelet transform is proposed for word boundary detection of isolated utterances. The sum of standard deviation of wavelet coefficients in the third coarse and weighted first detailed scale is defined as a new feature parameter for endpoint detection. We then developed a new and robust endpoint detection algorithm using the feature found in the wavelet domain. For the performance evaluation, we evaluated the detection accuracy and the average recognition error rate due to endpoint detection in an HMM-based recognition system across several signal-to-noise ratios and noise conditions.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.32 no.2
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• pp.147-156
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• 2013

In this paper, a new speech endpoint detection method in noisy environments for moving robot platforms is proposed. In the conventional method, the endpoint of speech is obtained by applying an edge detection filter that finds abrupt changes in the feature domain. However, since the feature of the frame energy is unstable in such noisy environments, it is difficult to accurately find the endpoint of speech. Therefore, a novel feature extraction method based on the twice-iterated fast fourier transform (TIFFT) and statistical models of speech is proposed. The proposed feature extraction method was applied to an edge detection filter for effective detection of the endpoint of speech. Representative experiments claim that there was a substantial improvement over the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.209-219
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• 2004

In order to implement a real time hardware for keyword spotting, we propose a Single-End-Point DTW(SEP-DTW) algorithm which is simple and less complex for computation. The SEP-DTW algorithm only needs a single end point which enables efficient applications, and it has a small wont of computations because the global search area is divided into successive local search areas. Also, we adopt new local constraints and a new distance measure for a better performance of the SEP-DTW algorithm. Besides, we make a normalization of feature same vectors so that they have the same variance in each frequency bin, and each frame has the same energy levels. To construct several reference patterns for each keyword, we use a clustering algorithm for all training patterns, and mean vectors in every cluster are taken as reference patterns. In order to detect a key word for input streams of speech, we measure the distances between reference patterns and input pattern, and we make a decision whether the distances are smaller than a pre-defined threshold value. With isolated speech recognition and keyword spotting experiments, we verify that the proposed algorithm has a better performance than other methods.

• The Journal of Korean Institute of Information Technology
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• v.17 no.8
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• pp.75-83
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• 2019

In this paper, we propose a method that can detect and trace the end point of real - time reinforcement steel to various environmental conditions of industrial field by using Median flow and Depth information. We proposed a method to derive two steel end points by using Median filter, Binarization, Morphology, and Blob algorithm on image depth information. The coordinates of the final position were determined by comparing the coordinates of the reinforcement steel endpoints detected in the Depth image and the position tracking coordinates of the reinforcement steel using Median Flow. As a result, when the existing Median Flow method was used, the success rate of the final position determination of reinforcement steel of 75% was increased to 95% when the Depth of reinforcement steel was used.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.91-94
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• 2002

음성인식 시스템의 실용화를 위해서 우선적으로 해결되어야 될 문제중 하나로 잡음환경하에서의 끝점검출을 들 수 있다. 잡음이 존재하지 않는 환경에서는 기존의 에너지 파라미터만으로도 어느정도 신뢰성있는 끝점 구간을 검출할 수 있으나 도심 소음과 같은 실제 잡음환경하에서는 대부분 좋지 않은 결과를 보인다. 본 논문에서는 도심환경의 배경잡음을 제거하는 방법으로 입력되는 음성에 대하여 주변소음에 의해 손상된 음성스펙트럼의 크기 성분만을 제거하는 전처리 기법인 Bark scale에 기반한 스펙트럼 차감법을 사용하고, 인간의 청각특성을 고려하여 음성의 주파수 대역을 3개의 대역으로 분리한 후, 대역별로 세밀한 에너지 문턱치값을 설정하여 음성의 끝점을 탐색하는 방법을 제안한다. 제안한 방법의 유효성을 확인하기 위해 실제 사무실 및 지하철역 등의 잡음환경하에서 녹음된 데이터베이스를 이용하여 끝점검출을 수행한 결과 기존의 에너지와 영교차율을 이용한 방법에 비해 평균 $46\%$의 오차율 감소와 대역에너지만을 사용한 경우에 비해 평균 $17\%$의 오차율 감소를 나타내어 제안한 방법의 유효성을 확인할 수 있었다.