A Bayesian Alternative to Gain Adaptation in Autoregressive Hidden Markov Models

Models dealing directly with the raw acoustic speech signal are an alternative to conventional feature-based HMMs. A popular way to model the raw speech signal is by means of an autoregressive (AR) process. Being too simple to cope with the nonlinearity of the speech signal, the AR process is generally embedded into a more elaborate model, such as the switching autoregressive HMM (SAR-HMM). A fundamental issue faced by models based on AR processes is that they are very sensitive to variations in the amplitude of the signal. One way to overcome this limitation is to use Gain Adaptation to adjust the amplitude by maximising the likelihood of the observed signal. However, adjusting model parameters by maximising test likelihoods is fundamentally outside the framework of standard statistical approaches to machine learning, since this may lead to overfitting when the models are sufficiently flexible. We propose a statistically principled alternative based on an exact Bayesian procedure in which priors are explicitly defined on the parameters of the AR process. Explicitly, we present the Bayesian SAR-HMM and compare the performance of this model against the standard Gain-Adapted SAR-HMM on a single digit recognition task, showing the effectiveness of the approach and suggesting thereby a principled and straightforward solution to the issue of Gain Adaptation

Adaptive Hidden Markov Noise Modelling for Speech Enhancement

A robust and reliable noise estimation algorithm is required in many speech enhancement systems. The aim of this thesis is to propose and evaluate a robust noise estimation algorithm for highly non-stationary noisy environments. In this work, we model the non-stationary noise using a set of discrete states with each state representing a distinct noise power spectrum. In this approach, the state sequence over time is conveniently represented by a Hidden Markov Model (HMM). In this thesis, we first present an online HMM re-estimation framework that models time-varying noise using a Hidden Markov Model and tracks changes in noise characteristics by a sequential model update procedure that tracks the noise characteristics during the absence of speech. In addition the algorithm will when necessary create new model states to represent novel noise spectra and will merge existing states that have similar characteristics. We then extend our work in robust noise estimation during speech activity by incorporating a speech model into our existing noise model. The noise characteristics within each state are updated based on a speech presence probability which is derived from a modified Minima controlled recursive averaging method. We have demonstrated the effectiveness of our noise HMM in tracking both stationary and highly non-stationary noise, and shown that it gives improved performance over other conventional noise estimation methods when it is incorporated into a standard speech enhancement algorithm