• Title, Summary, Keyword: Speech Enhancement

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Speech Enhancement Using Phase-Dependent A Priori SNR Estimator in Log-Mel Spectral Domain

  • Lee, Yun-Kyung;Park, Jeon Gue;Lee, Yun Keun;Kwon, Oh-Wook
    • ETRI Journal
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    • v.36 no.5
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    • pp.721-729
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    • 2014
  • We propose a novel phase-based method for single-channel speech enhancement to extract and enhance the desired signals in noisy environments by utilizing the phase information. In the method, a phase-dependent a priori signal-to-noise ratio (SNR) is estimated in the log-mel spectral domain to utilize both the magnitude and phase information of input speech signals. The phase-dependent estimator is incorporated into the conventional magnitude-based decision-directed approach that recursively computes the a priori SNR from noisy speech. Additionally, we reduce the performance degradation owing to the one-frame delay of the estimated phase-dependent a priori SNR by using a minimum mean square error (MMSE)-based and maximum a posteriori (MAP)-based estimator. In our speech enhancement experiments, the proposed phase-dependent a priori SNR estimator is shown to improve the output SNR by 2.6 dB for both the MMSE-based and MAP-based estimator cases as compared to a conventional magnitude-based estimator.

Performance Analysis of Noisy Speech Recognition Depending on Parameters for Noise and Signal Power Estimation in MMSE-STSA Based Speech Enhancement (MMSE-STSA 기반의 음성개선 기법에서 잡음 및 신호 전력 추정에 사용되는 파라미터 값의 변화에 따른 잡음음성의 인식성능 분석)

  • Park Chul-Ho;Bae Keun-Sung
    • MALSORI
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    • no.57
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    • pp.153-164
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    • 2006
  • The MMSE-STSA based speech enhancement algorithm is widely used as a preprocessing for noise robust speech recognition. It weighs the gain of each spectral bin of the noisy speech using the estimate of noise and signal power spectrum. In this paper, we investigate the influence of parameters used to estimate the speech signal and noise power in MMSE-STSA upon the recognition performance of noisy speech. For experiments, we use the Aurora2 DB which contains noisy speech with subway, babble, car, and exhibition noises. The HTK-based continuous HMM system is constructed for recognition experiments. Experimental results are presented and discussed with our findings.

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Global Soft Decision Using Probabilistic Outputs of Support Vector Machine for Speech Enhancement (SVM의 확률 출력을 이용한 새로운 Global Soft Decision 기반의 음성 향상 기법)

  • Jo, Q-Haing;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
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    • v.27 no.2
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    • pp.75-79
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    • 2008
  • In this paper, we propose a novel speech enhancement technique using global soft decision (GSD) based on the probabilistic outputs of support vector machine (SVM). Generally, speech enhancement algorithms applied soft decision gain modification and noise power estimation have bettor performance than those employing hard decision. Especially, global speech absence probability (GSAP), which is known as an effective measure of the speech absence in each frame, has been adopted to SD-based speech enhancement methods. For this reason, we introduce a new GSAP estimated from the probabilistic output of SVM using sigmoid function. The performance of the proposed algorithm is evaluated by the PESQ and MOS test under various noise environments and yields better results compared with the conventional GSD scheme.

Speech Estimators Based on Generalized Gamma Distribution and Spectral Gain Floor Applied to an Automatic Speech Recognition (잡음에 강인한 음성인식을 위한 Generalized Gamma 분포기반과 Spectral Gain Floor를 결합한 음성향상기법)

  • Kim, Hyoung-Gook;Shin, Dong;Lee, Jin-Ho
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.8 no.3
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    • pp.64-70
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    • 2009
  • This paper presents a speech enhancement technique based on generalized Gamma distribution in order to obtain robust speech recognition performance. For robust speech enhancement, the noise estimation based on a spectral noise floor controled recursive averaging spectral values is applied to speech estimation under the generalized Gamma distribution and spectral gain floor. The proposed speech enhancement technique is based on spectral component, spectral amplitude, and log spectral amplitude. The performance of three different methods is measured by recognition accuracy of automatic speech recognition (ASR).

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Comparison of Two Speech Estimation Algorithms Based on Generalized-Gamma Distribution Applied to Speech Recognition in Car Noisy Environment (자동차 잡음환경에서의 음성인식에 적용된 두 종류의 일반화된 감마분포 기반의 음성추정 알고리즘 비교)

  • Kim, Hyoung-Gook;Lee, Jin-Ho
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.8 no.4
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    • pp.28-32
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    • 2009
  • This paper compares two speech estimators under a generalized Gamma distribution for DFT-based single-microphone speech enhancement methods. For the speech enhancement, the noise estimation based on recursive averaging spectral values by spectral minimum noise is applied to two speech estimators based on the generalized Gamma distribution using $\kappa$=1 or $\kappa$=2. The performance of two speech enhancement algorithms is measured by recognition accuracy of automatic speech recognition(ASR) in car noisy environment.

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Probabilistic Target Speech Detection and Its Application to Multi-Input-Based Speech Enhancement (확률적 목표 음성 검출을 통한 다채널 입력 기반 음성개선)

  • Lee, Young-Jae;Kim, Su-Hwan;Han, Seung-Ho;Han, Min-Soo;Kim, Young-Il;Jeong, Sang-Bae
    • Phonetics and Speech Sciences
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    • v.1 no.3
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    • pp.95-102
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    • 2009
  • In this paper, an efficient target speech detection algorithm is proposed for the performance improvement of multi-input speech enhancement. Using the normalized cross correlation value between two selected channels, the proposed algorithm estimates the probabilistic distribution function of the value from the pure noise interval. Then, log-likelihoods are calculated with the function and the normalized cross correlation value to detect the target speech interval precisely. The detection results are applied to the generalized sidelobe canceller-based algorithm. Experimental results show that the proposed algorithm significantly improves the speech recognition performance and the signal-to-noise ratios.

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Two-Microphone Binary Mask Speech Enhancement in Diffuse and Directional Noise Fields

  • Abdipour, Roohollah;Akbari, Ahmad;Rahmani, Mohsen
    • ETRI Journal
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    • v.36 no.5
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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.

Speech Enhancement Using Receding Horizon FIR Filtering

  • Kim, Pyung-Soo;Kwon, Wook-Hyu;Kwon, Oh-Kyu
    • Transactions on Control, Automation and Systems Engineering
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    • v.2 no.1
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    • pp.7-12
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    • 2000
  • A new speech enhancement algorithm for speech corrupted by slowly varying additive colored noise is suggested based on a state-space signal model. Due to the FIR structure and the unimportance of long-term past information, the receding horizon (RH) FIR filter known to be a best linear unbiased estimation (BLUE) filter is utilized in order to obtain noise-suppressed speech signal. As a special case of the colored noise problem, the suggested approach is generalized to perform the single blind signal separation of two speech signals. It is shown that the exact speech signal is obtained when an incoming speech signal is noise-free.

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An Enhanced Clarity of Husky Voice by Dissonant Frequency Filtering

  • Kang, Sang-Ki;Baek, Seong-Joon
    • Speech Sciences
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    • v.12 no.4
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    • pp.71-76
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    • 2005
  • There have been numerous studies on the enhancement of noisy speech signal. In this paper, we propose a new speech enhancement method, that is, a filtering of a dissonant frequency combined with noise suppression algorithm. The simulation results indicate that the proposed method provides a significant gain in voice clarity. Therefore if the proposed enhancement scheme is used as a pre-filter, the perceptual clarity of husky voice is greatly enhanced.

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Minima Controlled Speech Presence Uncertainty Tracking Method for Speech Enhancement (음성 향상을 위한 최소값 제어 음성 존재 부정확성의 추적기법)

  • Lee, Woo-Jung;Chang, Joon-Hyuk
    • The Journal of the Acoustical Society of Korea
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    • v.28 no.7
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    • pp.668-673
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    • 2009
  • In this paper, we propose the minima controlled speech presence uncertainty tracking method to improve a speech enhancement. In the conventional tracking speech presence uncertainty, we propose a method for estimating distinct values of the a priori speech absence probability for different frames and channels. This estimation is inherently based on a posteriori SNR and used in estimating the speech absence probability (SAP). In this paper, we propose a novel estimation of distinct values of the a priori speech absence probability, which is based on minima controlled speech presence uncertainty tracking method, for different frames and channels. Subsequently, estimation is applied to the calculation of speech absence probability for speech enhancement. Performance of the proposed enhancement algorithm is evaluated by ITU-T P. 862 perceptual evaluation of speech quality (PESQ) under various noise environments. We show that the proposed algorithm yields better results compared to the conventional tracking speech presence uncertainty.