• 한국정보통신학회논문지
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• 제17권12호
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• pp.2793-2799
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• 2013

본 논문에서는 시간에 따라 변하는 잡음 환경에 강인한 음성 인식을 위해 효과적인 특징 보상 기법을 제안한다. 제안하는 기법에서는 기존의 Variational 모델 생성 기법의 모델 정확도를 향상시키고자 PCA를 도입한다. 제안된 기법은 다중 모델을 사용하는 PCGMM 기반의 특징 보상에 적용된다. 실험 결과는 제안한 PCA 기반의 Variational 모델 생성 기법이 배경 음악 환경의 다양한 SNR 조건에서 기존의 전처리 기법에 비하여 음성 인식 성능을 향상 시키는데 우수함을 입증한다. 제안한 모델 생성 기법이 기존의 Variational 모델 생성 방법에 비해 배경 음악 환경에서 평균 12.14%의 상대적 인식 성능 향상률을 나타낸다.

• 음성과학
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• 제12권3호
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• pp.201-209
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• 2005

This study was investigated developmental temporal features in Putonghua-speaking children. The total of 212 children between the ages 2;6 and 6;5 participated in Shanghai. Speech materials were constructed according to aspiration feature in stop sounds of Putonghua. Six words were selected in this study. A voice onset time was measured. Non-parametric procedures were employed for all the analyses. The VOT value across bilabial, alveolar, and velar stops was significantly differed between aspirated and unaspirated stops for each age group. Effect of age is. significant for unaspirated stops. It is clear that each of Putonghua stops showed decreasing mean and standard deviation. The overshoot phenomenon of VOT was apparent from the age of 2;6-2;11 to 4;6-4;11. There was high variability in the production of lag time for aspirated stops.

• 대한음성학회지:말소리
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• 제64호
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• pp.89-103
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• 2007

In this paper, we introduce mel-cepstrum modulation energy (MCME) for a feature to discriminate speech and music data. MCME is a mel-cepstrum domain extension of modulation energy (ME). MCME is extracted on the time trajectory of Mel-frequency cepstral coefficients, while ME is based on the spectrum. As cepstral coefficients are mutually uncorrelated, we expect the MCME to perform better than the ME. To find out the best modulation frequency for MCME, we perform experiments with 4 Hz to 20 Hz modulation frequency. To show effectiveness of the proposed feature, MCME, we compare the discrimination accuracy with the results obtained from the ME and the cepstral flux.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.65-68
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• 2003

In this paper, a speech recognition system using a speaker defendant algorithm is implemented on the PC. Results are loaded on a LDM display system that employs Intel StrongArm SA-1110. This research has completed so that this speech recognition system may correct its shortcomings. Sometimes a former system is operated by similar speech, not a same one. To input a vocalization is processed two times to solve mentioned defects. When references are creating, variable start-point and end-point are given to make efficient references. This references and new references are changed into feature parameter, LPC and MFCC. DTW is excuted using feature parameter. This security system will give user permission under fore execution have same result.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.123-126
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• 2007

This paper suggests a method to improve the performance of the pathological/normal voice classification. The effectiveness of the mel frequency-based filter bank energies using the fisher discriminant ratio (FDR) is analyzed. And mel frequency cepstrum coefficients (MFCCs) and the feature vectors through the linear discriminant analysis (LDA) transformation of the filter bank energies (FBE) are implemented. This paper shows that the FBE LDA-based GMM is more distinct method for the pathological/normal voice classification than the MFCC-based GMM.

• 대한음성학회지:말소리
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• 제45호
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• pp.35-45
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• 2003

Laryngeal diseases cause significant changes in the quality of speech production. Automatic detection of laryngeal diseases by voice is attractive because of its nonintrusive nature. In this paper, we apply speech recognition techniques to detection of laryngeal cancer, and investigate which feature parameters and classification methods are appropriate for this purpose. Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC) are examined as feature parameters, and parameters reflecting the periodicity of speech and its perturbation are also considered. As for classifier, multilayer perceptron neural networks and Gaussian Mixture Models (GMM) are employed. According to our experiments, higher order LPCC with the periodic information parameters yields the best performance.

• 공학논문집
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• 제2권1호
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• pp.13-21
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• 1997

본 연구는 연속음성을 인식하기 위하여 특징 Parameter의 변동성을 Fuzzy 변수로 취하여 Membership 함수로 표현한 후, Fuzzy 추론으로 연속음성을 인식하는 연구이다. 특징 Parameter로는 Formant 주파수, Pitch, Log Energy, Zero Crossing Rate등을 사용한다. 연속음성의 Data로서는 한국어의 연속음성을 대상으로 하여 음성인식 system을 구현한다음, 인식실험을 통하여 본 연구의 유교성을 확인한다.

• ETRI Journal
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• 제37권6호
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• pp.1211-1219
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• 2015

In a hidden Markov model-based speech synthesis system using a two-band excitation model, a maximum voiced frequency (MVF) is the most important feature as an excitation parameter because the synthetic speech quality depends on the MVF. This paper proposes an enhanced MVF estimation scheme based on a peak picking method. In the proposed scheme, both local peaks and peak lobes are picked from the spectrum of a linear predictive residual signal. The average of the normalized distances of local peaks and peak lobes is calculated and utilized as a feature to estimate an MVF. Experimental results of both objective and subjective tests show that the proposed scheme improves the synthetic speech quality compared with that of a conventional one in a mobile device as well as a PC environment.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.259-260
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• 2006

Since audio speech recognition is affected by noise in real environment, visual speech recognition is used to support speech recognition. For the visual speech recognition, this paper suggests the extraction of lip-feature using two types of image segmentation and reduced ASM. Input images are transformed to YCbCr based images and lips are segmented using the contrast of Y/Cb/Cr between lip and face. Subsequently, lip-shape model trained by PCA is placed on segmented lip region and then lip features are extracted using ASM.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2005년도 추계 학술대회 발표논문집
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• pp.59-62
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• 2005

Voice activity detection (VAD) is important in many areas of speech processing technology. Speech/nonspeech discrimination in noisy environments is a difficult task because the feature parameters used for the VAD are sensitive to the surrounding environments. Thus the VAD performance is severely degraded at low signal-to-noise ratios (SNRs). In this paper, a new VAD algorithm is proposed based on the degree of voicing and Quantile SNR (QSNR). These two feature parameters are more robust than other features such as energy and spectral entropy in noisy environments. The effectiveness of proposed algorithm is evaluated under the diverse noisy environments in the Aurora2 DB. According to out experiment, the proposed VAD outperforms the ETSI Advanced Frontend VAD.