In this letter, we propose a novel speech enhancement technique based on global soft decision incorporating a support vector machine (SVM). Global soft decision in the proposed approach is performed employing the probabilistic outputs of the SVM rather than the conventional Bayes' rule. Actually, global speech absence probability (GSAP) is determined by the sigmoid function based on key parameters estimated by the model-trust minimization algorithm of the SVM output. Improved results are obtained in terms of speech quality measures for various types of noise and at different signal-to-noise ratio (SNR) levels when the proposed SVM is adopted in the global soft decision for speech enhancement.

This letter presents a new voice activity detection (VAD) method using a uniformly most powerful (UMP) test to improve the performance of Sohn's et al.'s VAD. The UMP test is employed to derive a redefined decision rule based on the likelihood ratio test (LRT). The proposed method provides the decision rule by comparing the magnitude of the noisy speech signal to the adaptive threshold estimated from the noise statistics. Experimental results show that the proposed VAD algorithm based on the UMP test outperforms those based on the widely used Gaussian distribution.

This paper presents a new approach to online linear regression adaptation of continuous density hidden Markov models based on transformation space model (TSM) evolution. The TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression matrix parameters is effectively described in terms of the latent variable models such as the factor analysis or probabilistic principal component analysis. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. The proposed TSM evolution is a general framework with batch TSM adaptation as a special case. Experiments on supervised speaker adaptation demonstrate that the proposed approach is more effective compared with the conventional quasi-Bayes linear regression technique when a small amount of adaptation data is available.

This paper presents a new approach to online speaker adaptation based on transformation space model evolution. This approach extends the previous idea of speaker space model evolution by applying the a priori knowledge of training speakers to the speaker-dependent maximum likelihood linear regression (MLLR) matrix parameters. A quasi-Bayes (QB) estimation algorithm is devised to incrementally update the hyperparameters of the transformation space model and the regression matrices simultaneously. Experiments on supervised speaker adaptation demonstrate that the proposed approach is more effective compared with the conventional quasi-Bayes linear regression (QBLR) technique when a small amount of adaptation data is available.

We propose a method to enhance the performance of the extended maximum a posteriori (EMAP) estimation using the probabilistic principal component analysis (PPCA). PPCA is used to robustly estimate the correlation matrix among separate hidden Markov model (HMM) parameters. The correlation matrix is then applied to the EMAP scheme for speaker adaptation. PPCA is efficient to compute, and shows better performance compared to the method previously used for EMAP. Through various experiments on continuous digit recognition, it is shown that the EMAP approach based on the PPCA gives enhanced performance especially for a small amount of adaptation data.

In this letter, we propose a method to enhance the performance of the extended maximum a posteriori (EMAP) estimation using the probabilistic principal component analysis (PPCA). PPCA is used to robustly estimate the correlation matrix among separate hidden Markov model (HMM) parameters. The correlation matrix is then applied to the EMAP scheme for speaker adaptation. PPCA is efficient to compute and shows better performance compared to the method previously used for EMAP. Through various experiments on continuous digit recognition, it is shown that the EMAP approach based on the PPCA gives enhanced performance, especially for a small amount of adaptation data.

In this letter, we propose a rapid speaker adaptation technique based on the probabilistic principal component analysis (PPCA). The PPCA is employed to obtain the canonical speaker models that provide the a priori knowledge of the training speakers. The proposed approach is conveniently incorporated into the Bayesian adaptation framework, where the parameters are adapted to the new speaker's speech according to the maximum a posteriori (MAP) criterion. Through a number of continuous digit recognition experiments, we can find the effectiveness of the PPCA-based approach compared to the other adaptation approaches with a small amount of adaptation data.

In this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes it sufficient to adapt the parameters to the given FIR filter coefficients. Performance of the proposed technique is evaluated and compared to that of the retrained HMM parameters based on the continuous digit recognition experiments.