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We present a new approach to robust speech recognition based on structured modeling, irrelevant variability normalization (IVN) and unsupervised online adaptation (OLA). In offline training stage, a set of generic HMMs for basic speech units relevant to phonetic classification is trained along with several sets of feature transforms with different degrees of freedom by using a maximum likelihood (ML)...
In the past several years, we've been studying feature transformation (FT) approaches to robust automatic speech recognition (ASR) which can compensate for possible "distortions" caused by factors irrelevant to phonetic classification in both training and recognition stages. Several FT functions with different degrees of flexibility have been studied and the corresponding maximum likelihood...
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