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In our previous work, we proposed a feature compensation approach using high-order vector Taylor series (VTS) approximation for noisy speech recognition. In this paper, we report new progress on making it more powerful and practical in real applications. First, mixtures of densities are used to enhance the distortion models of both additive noise and convolutional distortion. New formulations for...
This paper presents a discriminative training approach to irrelevant variability normalization (IVN) based joint training of feature transforms and prototype-based classifier for recognition of online handwritten Chinese characters. A sample separation margin based minimum classification error criterion is adopted in IVN-based training, while an Rprop algorithm is used for optimizing the objective...
Recently, we proposed an i-vector approach to acoustic sniffing for irrelevant variability normalization based acoustic model training in large vocabulary continuous speech recognition (LVCSR). Its effectiveness has been confirmed by experimental results on Switchboard- 1 conversational telephone speech transcription task. In this paper, we study several discriminative feature extraction approaches...
This paper presents a discriminative training (DT) approach to irrelevant variability normalization (IVN) based training of feature transforms and hidden Markov models for large vocabulary continuous speech recognition. A speaker-clustering based method is used for acoustic sniffing and maximum mutual information (MMI) is used as a training criterion. Combined with unsupervised adaptation of feature...
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)...
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