• 음성과학
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• 제10권4호
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• pp.167-177
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• 2003

This paper presents a method of building an exceptional pronunciation dictionary for Korean automatic pronunciation generator. An automatic pronunciation generator is an essential element of speech recognition system and a TTS (Text-To-Speech) system. It is composed of a part of regular rules and an exceptional pronunciation dictionary. The exceptional pronunciation dictionary is created by extracting the words which have exceptional pronunciations from text corpus based on the characteristics of the words of exceptional pronunciation through phonological research and text analysis. Thus, the method contributes to improve performance of Korean automatic pronunciation generator as well as the performance of speech recognition system and TTS system.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2011년도 하계학술대회
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• pp.85-88
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• 2011

This paper describes a single channel speech enhancement as the pre-processor of automatic speech recognition system. The improvements are based on using optimally modified log-spectra (OM-LSA) gain function with a non-causal a priori signal-to-noise ratio (SNR) estimation. Experimental results show that the proposed method gives better perceptual evaluation of speech quality score (PESQ) and lower log-spectral distance, and also better word accuracy. In the enhancement system, parameters was turned for automatic speech recognition.

• 말소리와 음성과학
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• 제3권1호
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• pp.45-50
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• 2011

Formant structure of speech associated with five different emotions (anger, fear, happiness, neutral, sadness) was analysed. Acoustic separability of vowels (or emotions) associated with a specific emotion (or vowel) was estimated using F-ratio. According to the results, neutral showed the highest separability of vowels followed by anger, happiness, fear, and sadness in descending order. Vowel /A/ showed the highest separability of emotions followed by /U/, /O/, /I/ and /E/ in descending order. The acoustic results were interpreted and explained in the context of previous articulatory and perceptual studies. Suggestions for the performance improvement of an automatic emotion recognition system and automatic speech recognition system were made.

• 한국정보처리학회논문지
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• 제4권6호
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• pp.1531-1540
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• 1997

본 논문에서는 발화속도 측정과 이를 통한 보상방법을 통하여 성능 향상된 한국어 연속음 인식 시스템을 제안한다. 연속음 인식은 다양한 조음화 현상과 발화속도의 변화로 인하여 고립단어 인식에 비하여 어렵다. 따라서, 연속음 인식을 위해서는 조음화 현상과 발화속도의 변화를 모델링할 수 있는 방법이 필요하다. 본 논문에서는 발화속도를 포만트의 변화율로서 측정하였고, 이 정보를 이용하여 빠른 발화에서는 상대적으로 많은 특징벡터를 발생시켜 보상을 시도하였다. 또한 조음화 현상을 모델링하기 위하여 한국어의 다이폰 집합을 514개로 정의하였고, 훈련을 위한 음성 DB론느 ETRI의 445 단어 DB를 사용하였다. 이러한 방법을 결합한 한국어 연속음 인식기를 DHMM (Discrete Hidden Markov Model)으로 구현하여 인식률이 향상됨을 보였다.

• The Journal of the Acoustical Society of Korea
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• 제17권2E호
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• pp.38-46
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• 1998

In this paper, we propose a remote speech input device, a new method of user-friendly speech input in spontaneous speech recognition system. We focus the user friendliness on hands-free and microphone independence in speech recognition applications. Our method adopts two algorithms, the automatic speech detection and the microphone array delay-and-sum beamforming (DSBF)-based speech enhancement. The automatic speech detection algorithm is composed of two stages; the detection of speech and nonspeech using the pitch information for the detected speech portion candidate. The DSBF algorithm adopts the time domain cross-correlation method as its time delay estimation. In the performance evaluation, the speech detection algorithm shows within-200 ms start point accuracy of 93%, 99% under 15dB, 20dB, and 25dB signal-to-noise ratio (SNR) environments, respectively and those for the end point are 72%, 89%, and 93% for the corresponding environments, respectively. The classification of speech and nonspeech for the start point detected region of input signal is performed by the pitch information-base method. The percentages of correct classification for speech and nonspeech input are 99% and 90%, respectively. The eight microphone array-based speech enhancement using the DSBF algorithm shows the maximum SNR gaing of 6dB over a single microphone and the error reductin of more than 15% in the spontaneous speech recognition domain.

• 한국ITS학회 논문지
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• 제7권5호
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• pp.90-96
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• 2008

본 논문은 텔레메틱스 단말용 음성인식을 위한 음성향상 단일 칩 알고리듬을 제시한다. 제안된 방법은 잡음제거와 에코제거의 두 단계로 구성되어 있으며, 첫 단계로 크로스 스펙트럼 추정에 기반한 적응필터를 통해 에코를 제거하고, 두번째 단계로 Generalized Gamma분포기반의 LSA 음성추정 방식 추정을 통해 외부 배경잡음을 제거하여 음성의 음질을 향상시킨다. 적은 계산량이 요구되는 제안된 알고리즘을 토대로 구현된 단일 칩의 성능은 다양한 잡음환경에서 신호 대잡음비율과 음성인식 평가에서 기존의 방법보다 향상된 결과를 나타내었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2216-2219
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• 2003

Accurate speech region detection and automatic syllable segmentation is important part of speech recognition system. In automatic speech recognition system, they are needed for the purpose of accurate recognition and less computational complexity, In this paper, we Propose improved syllable segmentation method using ramp edge detection method and residual signal Peak energy. These methods were used to ensure accuracy and robustness for endpoint detection and syllable segmentation system. They have almost invariant response to various background noise levels. As experimental results, we obtained the rate of 90.7％ accuracy in syllable segmentation in a condition of accurate endpoint detection environments.

• 한국ITS학회 논문지
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• 제8권3호
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• pp.64-70
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• 2009

본 논문은 잡음에 강인한 음성인식 성능을 획득하기 위해 generalized Gamma 분포기반의 음성향상 기법을 제안한다. 우수한 음성향상을 위해서 제안된 방식에서는 generalized Gamma분포와 spectral gain floor를 이용한 음성추적 기법에 스펙트럼 최소잡음성분에 의한 희귀적인 평균 스펙트럼 값으로부터 유도되는 잡음추정을 결합하여 음질을 향상시켜 음성인식에 적용하였다. Spectral component, spectral amplitude 그리고 log spectral amplitude에 기반하여 제안된 음성향상 기법을 잡음환경에서의 음성인식에 적용하여 그 성능을 측정하였다.

• The Journal of the Acoustical Society of Korea
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• 제29권2E호
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• pp.86-99
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• 2010

This paper analyzes the performance of various single channel speech enhancement algorithms when they are applied to automatic speech recognition (ASR) systems as a preprocessor. The functional modules of speech enhancement systems are first divided into four major modules such as a gain estimator, a noise power spectrum estimator, a priori signal to noise ratio (SNR) estimator, and a speech absence probability (SAP) estimator. We investigate the relationship between speech recognition accuracy and the roles of each module. Simulation results show that the Wiener filter outperforms other gain functions such as minimum mean square error-short time spectral amplitude (MMSE-STSA) and minimum mean square error-log spectral amplitude (MMSE-LSA) estimators when a perfect noise estimator is applied. When the performance of the noise estimator degrades, however, MMSE methods including the decision directed module to estimate a priori SNR and the SAP estimation module helps to improve the performance of the enhancement algorithm for speech recognition systems.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 FIFTH WESTERN PACIFIC REGIONAL ACOUSTICS CONFERENCE SEOUL KOREA
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• pp.1082-1087
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• 1994

A set of text database is indispensable to the probabilistic models for speech recognition, linguistic model, and machine translation. We introduce an environment to canstruct text databases : an automatic tagging system and a set of tools for lexical knowledge acquisition, which provides the facilities of automatic part of speech recognition and guessing.