Frame Based Postprocessor for Speech Recognition Based on Augmented Conditional Random Fields

  • Yasser Hifny Faculty of computers and information systems, University of Helwan, Egypt
Keywords: Hidden Markov models, augmented conditional random fields, deep conditional random fields, speech recognition postprocessor.


In this paper, we present a novel postprocessor for speech recognition using the Augmented Conditional Random Field (ACRF) framework. In this framework, a primary acoustic model is used to generate state posterior scores per frame. These output scores are fed to the ACRF postprocessor for further frame based acoustic modeling.  Since ACRF explicitly integrates acoustic context modeling, the  postprocessor has the ability to discover new context information and to improve the recognition accuracy. The results on the TIMIT phone recognition task show that the proposed postprocessor can lead to significant improvements especially when Hidden Markov Models (HMMs) were used as  primary acoustic model.


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