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We present improvements to NeuroCRFs, a combination of neural network (NN) and conditional random fields (CRF) used for sequence labelling. The NN component is used to provide feature for label transitions that are then used by the CRF component to compute the likelihood. By exploiting the similarities between labels, we were able to add parameters shared by groups of similar label transitions. We...
Inspired by the success of deep neural network-hidden Markov model (DNN-HMM) in acoustic modeling for automatic speech recognition, a number of researchers from various fields have independently proposed the idea of combining DNN and conditional random fields (CRFs). Despite their subtle differences, this class of models is collectively referred to as “NeuroCRF” in this paper. We focus our attention...
This paper addresses the problem of open vocabulary automatic speech recognition (ASR) using hybrid statistical language models (LMs). Hybrid LMs differ from closed vocabulary LMs in that the word level lexicon is augmented with an inventory of sub-lexical units (SLUs). The procedures used for selecting these SLUs and expanding out-of-vocabulary (OOV) words according to the SLUs is presented in the...
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