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

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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.