Spectro-temporal post-enhancement using MMSE estimation in NMF based single-channel source separation

We propose to use minimum mean squared error (MMSE) estimates to enhance the signals that are separated by nonnegative matrix factorization (NMF). In single channel source separation (SCSS), NMF is used to train a set of basis vectors for each source from their training spectrograms. Then NMF is used to decompose the mixed signal spectrogram as a weighted linear combination of the trained basis vectors from which estimates of each corresponding source can be obtained. In this work, we deal with the spectrogram of each separated signal as a 2D distorted signal that needs to be restored. A multiplicative distortion model is assumed where the logarithm of the true signal distribution is modeled with a Gaussian mixture model (GMM) and the distortion is modeled as having a log-normal distribution. The parameters of the GMM are learned from training data whereas the distortion parameters are learned online from each separated signal. The initial source estimates are improved and replaced with their MMSE estimates under this new probabilistic framework. The experimental results show that using the proposed MMSE estimation technique as a post enhancement after NMF improves the quality of the separated signal

A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition

This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules leading to a unified view on known derivations as well as to new formulations for certain approaches. The generic Bayesian perspective provided in this contribution thus highlights structural differences and similarities between the analyzed approaches