• Speech Sciences
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• v.11 no.1
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• pp.43-52
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• 2004

This paper concerns an effective dual-channel noise reduction method to increase the performance of speech recognition in a car environment. While various single channel methods have already been developed and dual-channel methods have been studied somewhat, their effectiveness in real environments, such as in cars, has not yet been formally proven in terms of achieving acceptable performance level. Our aim is to remedy the low performance of the single and dual-channel noise reduction methods. This paper proposes an effective dual-channel noise reduction method based on a high-pass filter and front-end processing of the eigendecomposition method. We experimented with a real multi-channel car database and compared the results with respect to the microphones arrangements. From the analysis and results, we show that the enhanced eigendecomposition method combined with high-pass filter indeed significantly improve the speech recognition performance under a dual-channel environment.

• Phonetics and Speech Sciences
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• v.2 no.3
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• pp.141-148
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• 2010

In this paper, voice activity detection (VAD) for dual-channel noisy speech recognition is proposed in which spatial cues are employed. In the proposed method, a probability model for speech presence/absence is constructed using spatial cues obtained from dual-channel input signal, and a speech activity interval is detected through this probability model. In particular, spatial cues are composed of interaural time differences and interaural level differences of dual-channel speech signals, and the probability model for speech presence/absence is based on a Gaussian kernel density. In order to evaluate the performance of the proposed VAD method, speech recognition is performed for speech segments that only include speech intervals detected by the proposed VAD method. The performance of the proposed method is compared with those of several methods such as an SNR-based method, a direction of arrival (DOA) based method, and a phase vector based method. It is shown from the speech recognition experiments that the proposed method outperforms conventional methods by providing relative word error rates reductions of 11.68%, 41.92%, and 10.15% compared with SNR-based, DOA-based, and phase vector based method, respectively.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.411-412
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• 2007

본 논문에서는 인간 청각 시스템의 특징을 이용한 음성명료도 향상 알고리즘을 제안한다. 기존의 연구들은 음성과 잡음이 같이 섞여 있는 Single-Channel에서의 명료도 향상의 대해 주로 다루었다. 하지만 잡음에 섞이기 전의 깨끗한 음성과 주변 잡음이 분리된 Dual-Channel에서의 명료도 향상에 관한 연구는 거의 다루어지지 않았다. 본 논문에서 음성을 잡음이 섞이기 전에 미리 강화시켜 나중에 잡음에 섞였을 때 명료도가 강화되도록 하는 방법을 제안한다. 인간 청각 시스템의 마스킹 효과를 적절히 이용하여 음성을 강화시키는 방법을 사용하였다. 실험 결과 이 방법은 단순히 볼륨만을 높이는 방법에 비해 명료도가 더 향상되는 것으로 나타났다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1506-1513
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• 2019

Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI.

• Journal of the Ergonomics Society of Korea
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• v.25 no.2
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• pp.135-146
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• 2006

Multimodal interfaces are recognition-based technologies that interpret and encode hand gestures, eye-gaze, movement pattern, speech, physical location and other natural human behaviors. Modality is the type of communication channel used for interaction. It also covers the way an idea is expressed or perceived, or the manner in which an action is performed. Multimodal Interfaces are the technologies that constitute multimodal interaction processes which occur consciously or unconsciously while communicating between human and computer. So input/output forms of multimodal interfaces assume different aspects from existing ones. Moreover, different people show different cognitive styles and individual preferences play a role in the selection of one input mode over another. Therefore to develop an effective design of multimodal user interfaces, input/output structure need to be formulated through the research of human cognition. This paper analyzes the characteristics of each human modality and suggests combination types of modalities, dual-coding for formulating multimodal interaction. Then it designs multimodal language and input synchronization method according to the granularity of input synchronization. To effectively guide the development of next-generation multimodal interfaces, substantially cognitive modeling will be needed to understand the temporal and semantic relations between different modalities, their joint functionality, and their overall potential for supporting computation in different forms. This paper is expected that it can show multimodal interface designers how to organize and integrate human input modalities while interacting with multimodal interfaces.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.150-151
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• 2010

본 논문에서는 잡음환경에서의 이중채널 음성인식을 위한 통계모델 기반 음성구간 검출 방법을 제안한다. 제안된 방법에서는 다채널 입력 신호로부터 얻어진 공간정보를 이용하여 음성 존재 및 부재 확률모델을 구하고 이를 통해 음성구간 검출을 행한다. 이때, 공간정보는 두 채널간의 상호 시간 차이와 상호 크기 차이로, 음성 존재 및 부재 확률은 가우시안 커널 밀도 기반의 확률모델로 표현된다. 그리고 음성구간은 각 시간 프레임 별 음성 존재 확률 대비 음성 부재 확률의 비를 추정하여 검출된다. 제안된 음성구간 검출 방법의 평가를 위해 검출된 구간만을 입력으로 하는 음성인식 성능을 측정한다. 실험결과, 제안된 공간정보를 이용하는 통계모델 기반의 음성구간 검출 방법이 주파수 에너지를 이용하는 통계모델 기반의 음성구간 검출 방법과 주파수 스펙트럼 밀도 기반 음성구간 검출 방법에 비해 각각 15.6%, 15.4%의 상대적 오인식률 개선을 보였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.2
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• pp.237-245
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• 2012

This paper describes an acoustic echo canceller with double-talk by the blind signal separation. The acoustic echo canceller is deteriorated or diverged in the double-talk period. So we use the blind signal separation to estimate the near-end speech signal and to eliminate the estimated signal from the residual signal. The blind signal separation extracts the near-end signal with dual microphones by the iterative computations using the 2nd order statistical character. Because the mixture model of blind signal separation is multi-channel in the closed reverberation environment, we used the copied coefficients of echo canceller without computing the separation coefficients. By this method, the acoustic echo canceller operates irrespective of double-talking. We verified performances of the proposed acoustic echo canceller by simulations. The results show that the acoustic echo canceller with this algorithm detects the double-talk periods thoroughly, and then operates stably in the normal state without the divergence of coefficients after ending the double-talking. And it shows the ERLE of averagely 20dB higher than the normal LMS algorithm.