• Title/Summary/Keyword: 음성구간 검출

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Adaptive Noise Reduction of Speech Using Wavelet Transform (웨이브렛 변환을 이용한 음성의 적응 잡음 제거)

  • Lee, Chang-Ki;Kim, Dae-Ik
    • The Journal of the Korea institute of electronic communication sciences
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    • v.4 no.3
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    • pp.190-196
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    • 2009
  • A new time adapted threshold using the standard deviations of Wavelet coefficients after Wavelet transform by frame scale is proposed. The time adapted threshold is set up using the sum of standard deviations of Wavelet coefficient in level 3 approximation and weighted level 1 detail. Level 3 approximation coefficients represent the voiced sound with low frequency and level 1 detail coefficients represent the unvoiced sound with high frequency. After reducing noise by soft thresholding with the proposed time adapted threshold, there are still residual noises in silent interval. To reduce residual noises in silent interval, a detection algorithm of silent interval is proposed. From simulation results, it can be noticed that SNR and MSE of the proposed algorithm are improved than those of Wavelet transform and than those of Wavelet packet transform.

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Adaptive Noise Reduction of Speech using Wavelet Transform (웨이브렛 변환을 이용한 음성의 적응 잡음 제거)

  • Im Hyung-kyu;Kim Cheol-su
    • Journal of the Korea Computer Industry Society
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    • v.6 no.2
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    • pp.271-278
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    • 2005
  • This paper proposed a new time adapted threshold using the standard deviations of Wavelet coefficients after Wavelet transform by frame scale. The time adapted threshold is set up using the sum of standard deviations of Wavelet coefficient in level 3 approximation and weighted level 1 detail. Level 3 approximation coefficients represent the voiced sound with low frequency and level 1 detail coefficients represent the unvoiced sound with high frequency. After reducing noise by soft thresholding with the proposed time adapted threshold, there are still residual noises in silent interval. To reduce residual noises in silent interval, a detection algorithm of silent interval is proposed. From simulation results, it is demonstrated that the proposed algorithm improves SNR and MSE performance more than Wavelet transform and Wavelet packet transform does.

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Extraction of the shape feature according to the risk area of the segmented tumor region based on the small-animal PET (소동물 PET기반 종양분할영역 위험구간변화에 따른 형태특성추출)

  • Lee Joung-Min;Kim Hyeong-Min;Kim Myoung-Hee
    • Proceedings of the Korean Information Science Society Conference
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    • 2006.06b
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    • pp.376-378
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    • 2006
  • 본 논문에서는 소동물 양전자방출단층촬영 영상(Positron Emission Tomography, PET) 내 종양영역을 자동분할하고 분할된 윤곽선주변의 기하학적 위험구간에 따른 종양의 형태특성을 분석하기 위한 방법을 제시한다. PET 영상내 검출된 종양영역의 신뢰성을 위해 위음성(False negative, FN) 및 위양성(False positive, FP)의 위험구간을 같이 제공하는 것이 필요하다. 따라서, 방사선 특이적 특성이 반영된 명암값을 기반으로 Fuzzy C-Means(FCM) 클러스터링을 수행하여 종양영역을 자동 분할한다. 분활된 종양영역의 위험구간은 클러스터 간 공유되는 영역의 소속값을 이용하여 위음성, 위양성을 계산한다. 또한, 임의의 소속값 임계치 변화를 통해 위험구간의 변화에 따른 종양의 형태적 특성변화를 관측한다. 이러한 지역적 변화의 관측을 통해 위험구간의 형태학적 위치를 판단할 수 있어 위험구간에 따른 추가적인 잔여 암의 위치 및 형태 파악을 용이하게 한다.

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Voice Activity Detection in Noisy Environment using Speech Energy Maximization and Silence Feature Normalization (음성 에너지 최대화와 묵음 특징 정규화를 이용한 잡음 환경에 강인한 음성 검출)

  • Ahn, Chan-Shik;Choi, Ki-Ho
    • Journal of Digital Convergence
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    • v.11 no.6
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    • pp.169-174
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    • 2013
  • Speech recognition, the problem of performance degradation is the difference between the model training and recognition environments. Silence features normalized using the method as a way to reduce the inconsistency of such an environment. Silence features normalized way of existing in the low signal-to-noise ratio. Increase the energy level of the silence interval for voice and non-voice classification accuracy due to the falling. There is a problem in the recognition performance is degraded. This paper proposed a robust speech detection method in noisy environments using a silence feature normalization and voice energy maximize. In the high signal-to-noise ratio for the proposed method was used to maximize the characteristics receive less characterized the effects of noise by the voice energy. Cepstral feature distribution of voice / non-voice characteristics in the low signal-to-noise ratio and improves the recognition performance. Result of the recognition experiment, recognition performance improved compared to the conventional method.

Dimension Reduction Method of Speech Feature Vector for Real-Time Adaptation of Voice Activity Detection (음성구간 검출기의 실시간 적응화를 위한 음성 특징벡터의 차원 축소 방법)

  • Park Jin-Young;Lee Kwang-Seok;Hur Kang-In
    • Journal of the Institute of Convergence Signal Processing
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    • v.7 no.3
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    • pp.116-121
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    • 2006
  • In this paper, we propose the dimension reduction method of multi-dimension speech feature vector for real-time adaptation procedure in various noisy environments. This method which reduces dimensions non-linearly to map the likelihood of speech feature vector and noise feature vector. The LRT(Likelihood Ratio Test) is used for classifying speech and non-speech. The results of implementation are similar to multi-dimensional speech feature vector. The results of speech recognition implementation of detected speech data are also similar to multi-dimensional(10-order dimensional MFCC(Mel-Frequency Cepstral Coefficient)) speech feature vector.

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Speech Transition Detection and approximate-synthesis Method for Speech Signal Compression and Recovery (음성신호 압축 및 복원을 위한 음성 천이구간 검출과 근사합성 방식)

  • Lee, Kwang-Seok;Kim, Bong-Gi;Kang, Seong-Soo;Kim, Hyun-Deok
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2008.05a
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    • pp.763-767
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    • 2008
  • In a speech coding system using excitation source of voiced and unvoiced, it would be involved a distortion of speech qualify in case coexist with a voiced and an unvoiced consonants in a frame. So, We proposed TS(Transition Segment) including unvoiced consonant searching and extraction method in order to uncoexistent with a voiced and unvoiced consonants in a frame. This research present a new method of TS approximate-synthesis by using Least Mean Square and frequency band division. As a result, this method obtain a high quality approximation-synthesis waveforms within TS by using frequency information of 0.547kHz below and 2.813kHz above. The important thing is that the maximum error signal can be made with low distortion approximation-synthesis waveform within TS. This method has the capability of being applied to a new speech coding of Voiced/Silence/TS, speech analysis and speech synthesis.

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Development of a Read-time Voice Dialing System Using Discrete Hidden Markov Models (이산 HM을 이용한 실시간 음성인식 다이얼링 시스템 개발)

  • Lee, Se-Woong;Choi, Seung-Ho;Lee, Mi-Suk;Kim, Hong-Kook;Oh, Kwang-Cheol;Kim, Ki-Chul;Lee, Hwang-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.13 no.1E
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    • pp.89-95
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    • 1994
  • This paper describes development of a real-time voice dialing system which can recognize around one hundred word vocabularies in speaker independent mode. The voice recognition algorithm in this system is implemented on a DSP board with a telephone interface plugged in an IBM PC AT/486. In the DSP board, procedures for feature extraction, vector quantization(VQ), and end-point detection are performed simultaneously in every 10 msec frame interval to satisfy real-time constraints after detecting the word starting point. In addition, we optimize the VQ codebook size and the end-point detection procedure to reduce recognition time and memory requirement. The demonstration system has been displayed in MOBILAB of the Korean Mobile Telecom at the Taejon EXPO'93.

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A Discrete Feature Vector for Endpoint Detection of Speech with Hidden Markov Model (숨은마코프모형을 이용하는 음성 끝점 검출을 위한 이산 특징벡터)

  • Lee, Jei-Ky;Oh, Chang-Hyuck
    • The Korean Journal of Applied Statistics
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    • v.21 no.6
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    • pp.959-967
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    • 2008
  • The purpose of this paper is to suggest a discrete feature vector, robust in various levels of noisy environment and inexpensive in computation, for detection of speech segments and is to show such properties of the feature with real speech data. The suggested feature is one dimensional vector which represents slope of short term energies and is discretized into three values to reduce computational burden of computations in HMM. In experiments with speech data, the method with the suggested feature vector showed good performance even in noisy environments.

The Speech Enhancement of G.723.1 Vocoder by the Improvement of Pitch Accuracy Using a Flattened Energy in a Transient Period (전이구간에서의 Energy 평탄화를 통한 피치정확도 향상에 의한 G.723.1 Vocoder의 음질향상)

  • Park Won;Kim JungJin;Bae MyungJin
    • Proceedings of the Acoustical Society of Korea Conference
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    • spring
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    • pp.59-62
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    • 2000
  • 화상회의 및 인터넷폰을 목적으로 개발된 G.723.1은 CELP계열 보코더와 같이 화자의 개성정보를 위해 피치를 전송하고 있다. 하지만 안정구간과 비 안정구간의 차이를 두지 않고 처리를 하기 때문에 비 안정구간, 특히 전이구간에서 정확한 피치검출이 이루어지지 않는 이유로 음질의 열하가 발생하게 된다. 따라서 본 논문에서는 한 프레임 구간에서 에너지의 기울기로 대략적인 피치이득을 구한 다음 안정구간일 때와 프레임 내의 에너지의 기울기가 문턱 값을 넘을 때에는 기존의 방법으로 피치를 구하고 그런지 않은 경우에는 에너지를 조정하여 피치를 다시 구하는 방법을 사용하였다. 실제 음성시료에 대해 기존의 방법과 제안한 방법을 비교하기 위해 SegSNR 과 MOS를 비교하였을 때 각각 1.302(dB)와 평균 0.045 MOS가 향상되었다.

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시간특성을 고려한 음성신호의 발성율 검출에 관한 연구

  • 김익성;서지호;배명진
    • Proceedings of the Acoustical Society of Korea Conference
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    • spring
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    • pp.109-111
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    • 2004
  • 발성율은 일정한 시간동안 발성되는 음성신호 내에 몇 개의 음절이 포함되어 있는 지를 나타낸다. 발성율은 화자마다 다르고 각 음소들의 특징에 따라 변화할 수 있다. 발성율의 사전 측정이 이루어 진다면 음성부호화 측면에서도 중용한 정보로 사용될 수 있다. 기존의 음성부호화기는 발성율에 관계없이 고정적인 분석 구간을 정하여 전송률을 결정하고 있다. 따라서, 발성율을 미리 측정한다면, 발성율이 느린 부분과 빠른 부분에 각기 다른 부호화 방법을 적용하여 음질을 향상할 수도 있고 전송률을 가변적으로 적용할 수 도 있게 된다. 정확한 발성율을 측정하기 위해서는 음절의 변화를 추정하여야 한다. 음절의 변화를 추정하기 위한 방법으로 음성신호의 에너지 포락선 측정법과 LSP를 이용한 측정법이 각각 제안된 바 있으나, 본 논문에서는 위 두 가지 방법을 혼합한 방법을 사용하였다. 에너지 변동은 음성신호의 시간영역 처리방법으로 LSP 파라미터는 음성신호의 선형예측 분석에 의해 구해질 수 있다.

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