• 한국멀티미디어학회논문지
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• 제8권1호
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• pp.56-61
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• 2005

본 논문은 연속음성인식을 위한 음성구간과 피치를 검출하는 알고리즘을 제안한다. 이것은 연속음성을 입력받아 프레임 단위로 자/모음을 구분하며, 구분된 유성음에서 피치를 검출하는 방법이다 실제 잡음 환경에서 음성을 입력받아 적당한 문턱치 에너지를 사용함으로써 잡음환경에서 강인한 음성구간 추출이 가능하였고 추출한 음성구간에서 프레임단위로 영교차율과 단구간에너지를 이용한 알고리즘으로 유성음의 피치를 검출함과 동시에 자/모음을 구분하는 개선된 방식이다.

• ETRI Journal
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• 제25권1호
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• pp.49-51
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• 2003

Pitch estimation is important in various speech research areas, but when the speech is noisy, accurate pitch estimation with conventional pitch detectors is almost impossible. To solve this problem, we propose a new pitch detection algorithm for noisy speech using a noise whitening technique on the background noise and obtain successful results.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1993년도 학술논문발표회 논문집 제12권 1호
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• pp.88-92
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• 1993

In speech signal processing, it is necessary to detect exactly the pitch. The algorithms of pitch extraction which have been proposed until now are difficult to detect pitches over wide range speech signals. Thus we propose a new algorithm which uses the G-peak extraction to do it. It is the method that finds the most MZI(maximum zero-crossing interval) at each frame and convolve it with speech signal ; this is the same with passing speech signals to variable LPF. Finally we obtained the pitch, improve the accuracy of pitch detection and extract it with the high speed.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1987년도 학술발표회 논문집
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• pp.86-88
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• 1987

Accurate detection of the voicved segment in speech signals is important for robust pitch extraction. This paper describes an explicit detection algorithmfor detecting the voiced segment in speech signals. Thsi algoithm is based on the fluctuation properties of maximum magnitudes in each frame of speech signals. The performance of this detector is evaluated and compared to that obtained from manually classifying 150 recorded digit utterances.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2006년도 춘계 학술대회 발표논문집
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• pp.45-48
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• 2006

As the speech recognition systems are used in many emerging applications, robust performance of speech recognition systems under extremely noisy conditions become more important. The voice activity detection (VAD) has been taken into account as one of the important factors for robust speech recognition. In this paper, we investigate conventional VAD algorithms and analyze the weak and the strong points of each algorithm.

• Journal of information and communication convergence engineering
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• 제1권4호
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• pp.205-208
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• 2003

Voice activity detection is a important problem in the speech recognition and speech communication. This paper introduces new feature parameter which are reconstructed by spectral entropy of information theory for robust voice activity detection in the noise environment, then analyzes and compares it with energy method of voice activity detection and performance. In experiments, we confirmed that spectral entropy and its reconstructed parameter are superior than the energy method for robust voice activity detection in the various noise environment.

• International Journal of Internet, Broadcasting and Communication
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• 제11권4호
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• pp.26-30
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• 2019

This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

• 한국멀티미디어학회논문지
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• 제4권1호
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• pp.46-55
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• 2001

본 논문에서는 확장 스펙트럼 차감 알고리즘으로 처리된 신호의 추정 신호 대 잡음비를 이용한 새로운 음성 활동 검출법을 제안한다. 확장 스펙트럼 차감 알고리즘의 Wiener필터 출력 신호에서 신호 대 잡음비를 추정하기 위한 Wiener 필터를 하나 더 둠으로써 음성 활동을 검출한다. 제안하는 음성 활동 검출기는 계산량이 많지 않으며 낮은 신호 대 잡음비에서도 잘 동작했다. 제안하는 음성 활동 검출기의 응용으로 Boll의 스펙트럼 차감 알고리즘에 제안하는 음성 활동 검출기를 적용한 다음 확장 스펙트럼 차감 알고리즘과 비교하였다. 제안하는 음성 활동 검출법에 의한 Boll의 스펙트럼 타감 알고리즘은 음성/비음성 구간 모두에서 확장스펙트럼 차감 알고리즘보다 우수한 성능을 보였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.37-39
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. Since the background noise presenting to record will introduce disturbance while we just want to get the stationary parameters to represent the corresponding speech section, in particular, a major source of error in automatic recognition system of isolated words is the inaccurate detection of beginning and ending boundaries of test and reference templates, thus we must find potent method to remove the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two simple time-domain measurements - short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Comparing with the conventional methods based on short-time energy and short-time zero-crossing rate, the novel approach based on time-domain Lyapunov Exponents(LEs) is low complexity and suitable for Digital Isolated Word Recognition System.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.89-92
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• 2005

Using natural speech commands for controlling a human-robot is an interesting topic in the field of robotics. In this paper, our main focus is on the verification of a speaker who gives a command to decide whether he/she is an authorized person for commanding. Among possible dynamic features of natural speech, pitch period is one of the most important ones for characterizing speech signals and it differs usually from person to person. However, current techniques of pitch detection are still not to a desired level of accuracy and robustness. When the signal is noisy or there are multiple pitch streams, the performance of most techniques degrades. In this paper, we propose a two-level approach for pitch detection which in compare with standard pitch detection algorithms, not only increases accuracy, but also makes the performance more robust to noise. In the first level of the proposed approach we discriminate voiced from unvoiced signals based on a neural classifier that utilizes cepstrum sequences of speech as an input feature set. Voiced signals are then further processed in the second level using a modified standard AMDF-based pitch detection algorithm to determine their pitch periods precisely. The experimental results show that the accuracy of the proposed system is better than those of conventional pitch detection algorithms for speech signals in clean and noisy environments.