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Model adaptation techniques are an efficient way to reduce the mismatch that typically occurs between the training and test condition of any automatic speech recognition (ASR) system. This work addresses the problem of increased degradation in performance when moving from speaker-dependent (SD) to speaker-independent (SI) conditions for connectionist (or hybrid) hidden Markov model/artificial neural...
In this paper, we propose a novel acoustic model adaptation method for noise robust speech recognition. Model combination is a common way to adapt acoustic models to a target test environment. For example, the mean supervectors of the adapted model are obtained as a linear combination of mean supervectors of many pre-trained environment-dependent acoustic models. Usually, the combination weights are...
By explicitly modelling the distortion of speech signals, model adaptation based on vector Taylor series (VTS) approaches have been shown to significantly improve the robustness of speech recognizers to environmental noise. However, the computational cost of VTS model adaptation (MVTS) methods hinders them from being widely used because they need to adapt all the HMM parameters for every utterance...
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