Noise adaptive training for subspace Gaussian mixture models

Noise adaptive training (NAT) is an effective approach to normalise the environmental distortions in the training data. This paper investigates the model-based NAT scheme using joint uncertainty decoding (JUD) for subspace Gaussian mixture models (SGMMs). A typical SGMM acoustic model has much larger number of surface Gaussian components, which makes it computationally infeasible to compensate each Gaussian explicitly. JUD tackles the problem by sharing the compensation parameters among the Gaussians and hence reduces the computational and memory demands. For noise adaptive training, JUD is reformulated into a generative model, which leads to an efficient expectation-maximisation (EM) based algorithm to update the SGMM acoustic model parameters. We evaluated the SGMMs with NAT on the Aurora 4 database, and obtained higher recognition accuracy compared to systems without adaptive training. Index Terms: adaptive training, noise robustness, joint uncertainty decoding, subspace Gaussian mixture model

Subband particle filtering for speech enhancement

Journal ArticleABSTRACT Particle filters have recently been applied to speech enhancement when the input speech signal is modeled as a time-varying autoregressive process with stochastically evolving parameters. This type of modeling results in a nonlinear and conditionally Gaussian statespace system that is not amenable to analytical solutions. Prior work in this area involved signal processing in the fullband domain and assumed white Gaussian noise with known variance. This paper extends such ideas to subband domain particle filters and colored noise. Experimental results indicate that the subband particle filter achieves higher segmental SNR than the fullband algorithm and is effective in dealing with colored noise without increasing the computational complexity

Paraunitary oversampled filter bank design for channel coding

Oversampled filter banks (OSFBs) have been considered for channel coding, since their redundancy can be utilised to permit the detection and correction of channel errors. In this paper, we propose an OSFB-based channel coder for a correlated additive Gaussian noise channel, of which the noise covariance matrix is assumed to be known. Based on a suitable factorisation of this matrix, we develop a design for the decoder's synthesis filter bank in order to minimise the noise power in the decoded signal, subject to admitting perfect reconstruction through paraunitarity of the filter bank. We demonstrate that this approach can lead to a significant reduction of the noise interference by exploiting both the correlation of the channel and the redundancy of the filter banks. Simulation results providing some insight into these mechanisms are provided