• 음성과학
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• 제5권1호
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• pp.63-80
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• 1999

In this paper, an adaptive method of dividing a speech signal into an initial, a medial and a final sound of the form of utterance utilized by evaluating extreme limits of short term energy and autocorrelation functions. By applying this method into speech signal composed of a consonant, a vowel and a consonant, it was divided into an initial, a medial and a final sound and its feature analysis of sample by LPC were carried out. As a result of spectrum analysis in each period, it was observed that there existed spectrum features of a consonant and a vowel in the initial and medial periods respectively and features of both in a final sound. Also, when all kinds of words were adaptively divided into 3 periods by using the proposed method, it was found that the initial sounds of the same consonant and the medial sounds of the same vowels have the same spectrum characteristics respectively, but the final sound showed different spectrum characteristics even if it had the same consonant as the initial sound.

• ETRI Journal
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• 제36권5호
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• pp.772-782
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• 2014

Two-microphone binary mask speech enhancement (2mBMSE) has been of particular interest in recent literature and has shown promising results. Current 2mBMSE systems rely on spatial cues of speech and noise sources. Although these cues are helpful for directional noise sources, they lose their efficiency in diffuse noise fields. We propose a new system that is effective in both directional and diffuse noise conditions. The system exploits two features. The first determines whether a given time-frequency (T-F) unit of the input spectrum is dominated by a diffuse or directional source. A diffuse signal is certainly a noise signal, but a directional signal could correspond to a noise or speech source. The second feature discriminates between T-F units dominated by speech or directional noise signals. Speech enhancement is performed using a binary mask, calculated based on the proposed features. In both directional and diffuse noise fields, the proposed system segregates speech T-F units with hit rates above 85%. It outperforms previous solutions in terms of signal-to-noise ratio and perceptual evaluation of speech quality improvement, especially in diffuse noise conditions.

• 한국철도학회논문집
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• 제14권2호
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• pp.130-136
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• 2011

본 논문에서는 철도예약서비스를 위한 SIP를 기반으로 하는 텔레포니 환경에서의 VoiceXML을 이용한 실시간 음성인식을 구현하는 방안을 제안하였다. 제안된 방법은 PSTN 또는 인터넷을 통하여 들어온 음성신호를 VoiceXML을 이용한 Dialog 처리를 하고 전송된 음성신호를 음성인식 시스템에서 처리하여 출력된 결과값을 VoiceXML의 Dialog에 반환하여 사용자에게 전달하는 방식이다. VASR 시스템은 Dialog를 처리하는 Dialog 서버, 음성신호를 처리하기 위한 APP서버, 그리고 음성인식을 처리하는 음성인식 시스템으로 구성된다. 본 논문에서는 텔레포니 환경에서의 음성신호 처리를 위하여 VoiceXML의 Record Tag 기능을 이용하여 음성신호를 녹음하고 이를 실시간 재생하여 음성인식 시스템으로 전송하는 방식을 구현하였다.

• 융합신호처리학회논문지
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• 제7권4호
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• pp.178-183
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• 2006

As a lot of researches on the speech signal processing are performed due to the recent rapid development of the information-communication technology. the pitch period is used as an important element to various speech signal application fields such as the speech recognition. speaker identification. speech analysis. or speech synthesis. A variety of algorithms for the time and the frequency domains related with such pitch period detection have been suggested. One of the pitch detection algorithms for the time domain. AMDF (average magnitude difference function) uses distance between two valley points as the calculated pitch period. However, it has a problem that the algorithm becomes complex in selecting the valley points for the pitch period detection. Therefore, in this paper we proposed the modified AMDF(M-AMDF) algorithm which recognizes the entire minimum valley points as the pitch period of the speech signal by using the rotation transform of AMDF. In addition, a threshold is set to the beginning portion of speech so that it can be used as the selection criteria for the pitch period. Moreover the proposed algorithm is compared with the conventional ones by means of the simulation, and presents better properties than others.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.150-155
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• 2006

Speech intelligibility is investigated on a conference room-window glass coupled system. Using MLS(Maximum Length Sequency) signal as a sound source, acceleration and velocity responses of the window glass are measured by accelerometer and laser doppler vibrometer. MTF(Modulation Transfer Function) is used to identify the speech transmission characteristics of the room and window system. STI(Speech Transmission Index) is calculated by using MTF and speech intelligibility of the room and the window glass is estimated. Speech intelligibilities by the acceleration signal and the velocity signal are compared and the possibility of the wiretapping is investigated. Finally, intelligibility of the conversation sound is examined by the subjective test.

• ETRI Journal
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• 제38권2호
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• pp.366-375
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• 2016

This paper describes an algorithm to suppress composite noise in a two-microphone speech enhancement system for robust hands-free speech communication. The proposed algorithm has four stages. The first stage estimates the power spectral density of the residual stationary noise, which is based on the detection of nonstationary signal-dominant time-frequency bins (TFBs) at the generalized sidelobe canceller output. Second, speech-dominant TFBs are identified among the previously detected nonstationary signal-dominant TFBs, and power spectral densities of speech and residual nonstationary noise are estimated. In the final stage, the bin-wise output signal-to-noise ratio is obtained with these power estimates and a Wiener post-filter is constructed to attenuate the residual noise. Compared to the conventional beamforming and post-filter algorithms, the proposed speech enhancement algorithm shows significant performance improvement in terms of perceptual evaluation of speech quality.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2001년도 추계학술발표회 논문집
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• pp.75-76
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• 2001

An endpoint detection method for speech signals utilizing hidden Markov model(HMM) is proposed. It turns out that the proposed algorithm is quite satisfactory to apply isolated word speech recognition.

• 대한전자공학회논문지SP
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• 제47권1호
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• pp.175-182
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• 2010

본 논문에서는 배경 잡음 환경에서 배경 잡음에 의해 저하된 음성 신호의 명료도를 soft decision 기반의 음성 부재 확률을 이용하여 음성 강화 이득을 수정함으로써 음성의 명료도를 보다 향상시키는 기법을 제안한다. 배경 잡음 환경에서 저하된 음성의 명료도를 향상시키기 위한 기존의 음성 강화 기법으로써 soft decision을 이용하여 오염된 음성 신호로부터 깨끗한 음성 신호만 증폭시키는 알고리즘이 제안되었다. 기존의 음성 강화 기법 보다 음성 구간과 비음성 구간 및 전이 구간에서 강인한 음성 강화 이득을 추정하기 위하여 soft decision 기반의 음성 부재 확률 (Speech Absence Probability)을 음성 강화 이득에 통합한 음성 강화 이득 수정 알고리즘을 제안한다. 제안된 음성 강화 기법의 성능은 다양한 배경 잡음 환경에서 ITU-T P.800의 주관적인 음질 측정 방법인 (Comparison Category Rating) 테스트에 의해서 평가되었으며, 기존의 음성 강화 기법과 비교하여 향상된 성능을 보여주었다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.47-50
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• 2015

음성 신호는 자음 신호과 모음 신호의 결합으로 이루어져 있지만 그 특성상 자음보다는 모음 신호의 지속시간이 길다. 따라서 전체적으로 음성 신호 블록들 사이의 상관관계가 상당히 크다고 간주할 수 있다. 음성신호를 128개의 데이터를 갖는 블록들로 나눈 후 각 블록의 FFT를 구한다. 이 중에서 모음의 에너지가 집중되어 있는 저주파수 부분만 취하여 이웃 블록들과의 공분산 행렬을 구하고 이 행렬로부터 고유값을 계산해 낸다. 이 중 첫 번 째 고유값은 주성분과 관련이 있다. 다양한 음성파일들을 이용하여 비교적 값이 큰 첫 번째, 두 번째, 세 번째 고유값과 이들을 합한 고유값이 각 음성 파일에서 어떻게 나타나는지 그 분포를 알아보고 이것들이 음성신호가 아닌 자동차 소음 신호와 같은 잡음 신호의 고유값 분포와 어떻게 다른지 분석한다.