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Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and convolutional...
We propose the prediction-adaptation-correction RNN (PAC-RNN), in which a correction DNN estimates the state posterior probability based on both the current frame and the prediction made on the past frames by a prediction DNN. The result from the main DNN is fed back to the prediction DNN to make better predictions for the future frames. In the PAC-RNN, we can consider that, given the new, current...
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...
Current Statistical Machine Translation (SMT) systems translate one sentence at a time, ignoring any document level information. Consequently, translation models are learned only at sentence level and document contexts are generally overlooked. In this paper, we try to introduce document topic to help SMT system to produce target sentences. First, the parallel training corpus with underlying document...
In HMM-based speech synthesis, we usually use complex, context dependent models to characterize prosodically and linguistically rich speech units. It is therefore difficult to prepare training data which can cover all combinatorial possibilities of contexts. A common approach to cope with this insufficient training data problem is to build a clustered tree via the MDL criterion. However, an MDL-based...
We present an evidence Bayesian framework, which can learn both the prior distributions and posterior distributions from data, for continuous-density hidden Markov models (CDHMM). The goal of this study is to build the regularized CDHMMs to improve model generalization, and achieve desirable recognition performance for unknown test speech. Under this framework, we develop an EM iterative procedure...
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