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Speech recognition performance deteriorates drastically when they are deployed in practical situation where the speech is corrupted by additive noise. One way to improve the robustness of the speech recognition system is to enhance the speech prior to its recognition. This paper focuses on developing a masking-based s-order minimum mean square error (s-masking MMSE) speech enhancement for speech recognition under noise condition. Addressing the artifacts introduced by enhancement algorithm and the remaining noise after denoising, we modified the estimation algorithm of spectral parameters for the s-masking MMSE by controlling the power of processing noise, strengthening the weak signal processing, oversuppressing the residual noise and reestimating a priori SNR. The evaluation shows the proposed enhancement scheme is significantly effective to improve the performance of state-of-the-art speech recognition.

Speech recognition performance deteriorates in face of unknown noise. Speech enhancement offers a solution by reducing the noise in speech at runtime. However, it also introduces artificial distortions to the speech signals. In this paper, we aim at reducing the artifacts that has adverse effects on speech recognition. With this motivation, we propose a modification scheme including smoothing adaptation to frame SNR and reestimation of a priori SNR for spectral-domain log-spectral-amplitude (LSA) speech enhancement. The experiments show that the proposed scheme of enhancement significantly improves the performance of the state-of-the-art speech recognition over the baseline speech enhancement.

Gaussian mixture model (GMM) and support vector machine (SVM) have become popular classifiers in text-independent speaker recognition. A GMM-supervector characterizes a speaker's voice with the parameters of GMM, which include mean vectors, covariance matrices, and mixture weights. GMM-supervector SVM benefits from both GMM and SVM frameworks to achieve the state-of-the-art performance. Conventional Kullback-Leibler (KL) kernel in GMM-supervector SVM classifier limits the adaptation of GMM to mean value and leaves covariance unchanged. In this letter, we introduce the GMM-UBM mean interval (GUMI) concept based on the Bhattacharyya distance. This leads to a new kernel for SVM classifier. Comparing with the KL kernel, the new kernel allows us to exploit the information not only from the mean but also from the covariance. We demonstrate the effectiveness of the new kernel on the 2006 National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) dataset.

A signal subspace scheme based on masking properties is proposed for enhancement of speech degraded by additive noise. Since the masking properties are related to the critical frequency band that is derived from the characteristics of human cochlea, the incorporation of masking threshold into a subspace technique requires the transformation between the frequency and eigen domains. We present and apply an invertible transformation between the frequency and eigen domains. In this paper, we use masking properties of the human auditory system to define the audible noise quantity in the eigendomain. We derive the eigen-decomposition of the estimated speech autocorrelation matrix with the assumption of white noise. Subsequently, an audible noise reduction scheme is developed based on a signal subspace technique, and the implementation of our proposed scheme is outlined. We further extend the scheme to the colored noise case. Simulation results show the superiority of our proposed scheme over other existing subspace methods in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ), modified Bark spectral distortion (MBSD), spectrogram and informal listening tests.

This paper proposes /spl beta/-order minimum mean-square error (MMSE) speech enhancement approach for estimating the short time spectral amplitude (STSA) of a speech signal. We analyze the characteristics of the /spl beta/-order STSA MMSE estimator and the relation between the value of /spl beta/ and the spectral amplitude gain function of the MMSE method. We further investigate the effectiveness of a range of fixed-/spl beta/ values in estimating STSA based on the MMSE criterion, and discuss how the /spl beta/ value could be adapted using the frame signal-to-noise ratio (SNR). The performance of the proposed speech enhancement approach is then evaluated through spectrogram inspection, objective speech distortion measures and subjective listening tests using several types of noise sources from the NOISEX-92 database. Evaluation results show that our approach can achieve a more significant noise reduction and a better spectral estimation of weak speech spectral components from a noisy signal as compared to many existing speech enhancement algorithms.

Masking properties have been widely exploited in speech enhancement techniques, especially those implemented in the spectral domain. The incorporation of auditory masking in a subspace technique invariably requires a transformation linking the frequency and eigendomains. In this letter, an invertible transformation between the frequency and eigendomains is derived. The proposed transformation is verified through a conventional masking-based subspace speech-enhancement method. Simulation results show that our proposed transformation for speech enhancement outperforms the conventional transformation in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ), and listening tests.

Recently, joint factor analysis (JFA) and identity-vector (i-vector) represent the dominant techniques used for speaker recognition due to their superior performance. Developed relatively earlier, the Gaussian mixture model - support vector machine (GMM-SVM) with nuisance attribute projection (NAP) has gradually become less popular. However, when developing the relevance factor in maximum a posteriori (MAP) estimation of GMM to be adapted by application data in place of the conventional fixed value, it is noted that GMM-SVM demonstrates some advantages. In this paper, we conduct a comparative study between GMM-SVM with adaptive relevance factor and JFA/i-vector under the framework of Speaker Recognition Evaluation (SRE) formulated by the National Institute of Standards and Technology (NIST).

International audience; This paper advocates the use of probabilistic linear discriminant analysis (PLDA) for partially open-set detection task with multiple i-vectors enrollment condition. Also referred to as speaker verification, the speaker detection task has always been considered under an open-set scenario. In this paper, a more general partially open-set speaker detection problem in considered, where the imposters might be one of the known speakers previously enrolled to the system. We show how this could be coped with by modifying the definition of the alternative hypothesis in the PLDA scoring function. We also look into the impact of the conditional-independent assumption as it was used to derive the PLDA scoring function with multiple training i-vectors. Experiments were conducted using the NIST 2012 Speaker Recognition Evaluation (SRE'12) datasets to validate various points discussed in the paper.

In this paper, we study speech enhancement in eigen-domain. In our previous work on audible noise reduction, we use masking properties of the human auditory system to define the audible noise quantity in the eigen-domain. We then derived the speech enhancement algorithm using white noise model. Without loss of generality, in this paper, we introduce the noise reduction method for colored noise. Through many simulations, we show that colored noise modeling is superior over other existing eigen-decomposition methods in terms of objective and subjective evaluations.

In this paper, we investigate the contribution of the speech enhancement to telephony name speech recognition system that has been of the feature enhancement in noisy environment. Since the masking based subband Kalman filtering (MSKF) method has been shown to have good performance over many existing speech enhancement methods for human listening, we especially examine the MSKF speech enhancement method for the speech recognition system. We evaluate the performance of the speech enhancement methods based on the recorded telephony name speech database. Experimental results show that the recognition error rate of the speech recognition system can be reduced much by using MSKF.

In this paper, autoregressive parameter estimation for Kalman filtering speech enhancement is studied. In conventional Kalman filtering speech enhancement, spectral subtraction is usually used for speech autoregressive (AR) parameter estimation. We propose log spectral amplitude (LSA) minimum mean-square error (MMSE) instead of spectral subtraction for the estimation of speech AR parameters. Based on an observation that full-band Kalman filtering speech enhancement often causes an unbalanced noise reduction between speech and non-speech segments, a spectral solution is proposed to overcome the unbalanced reduction of noise. This is done by shaping the spectral envelopes of the noise through likelihood ratio. Our simulation results show the effectiveness of the proposed method.

A novel subspace-based speech enhancement scheme, based on a criterion of audible noise reduction, is considered. The masking properties of the human auditory system are used to define the audible noise quantity in the eigen-domain. Subsequently, an audible noise reduction scheme is developed based on a signal subspace technique. We derive the eigendecomposition of the estimated speech autocorrelation matrix with the assumption of white noise and outline the implementation of our proposed scheme. We further extend the scheme to the colored noise case. Simulation results show that our proposed scheme outperforms many existing subspace methods in terms of segmental signal-to-noise ratio (SNR), perceptual evaluation of speech quality (PESQ) and informal listening tests.

To enhance the noisy speech signal, a perceptual weighting based Kalman filtering is investigated. Our study is to seek a high perceptual quality of speech enhancement system which optimizes the trade-off between the speech distortion and noise reduction. Using perceptual weighting to replace the masking threshold avoids the frequency domain complexity, it is suitable for time domain Kalman filtering to estimate the state-space vector in only the time domain. Through many simulations, it is demonstrated that the proposed perceptual Kalman filtering outperforms the conventional Kalman filtering

This thesis focuses on the development of robust speech enhancement methods based on masking properties of the human auditory system. DOCTOR OF PHILOSOPHY (EEE)

In this paper a single channel speech enhancement system based on Kalman filtering and masking properties of the human auditory system is studied. The main objective of our study is to develop a high quality speech enhancement system for car mobile communication, which optimizes the tradeoff between speech distortion and noise reduction. To develop a robust Kalman filtering speech enhancement system, we propose that the noise variance estimate be modified as a function of masking threshold in each subband. By using such a Kalman filtering with masking method, we can achieve better results as compared to full band Kalman filtering, especially in the case of weak speech spectral components in noise. Through a great deal of simulations, we show the advantages of our proposed system in terms of objective and subjective measurements as compared to many existing speech enhancement methods