Classification of asr word hypotheses using prosodic information and resampling of training data

摘要

In this work, we propose a novel resampling method based on word lattice information and we use prosodic cues with support vector machines for classification. The idea is to consider word recognition as a two-class classification problem, which considers the word hypotheses in the lattice of a standard recognizer either as True or False employing prosodic information. The technique developed in this paper was applied to set of words extracted from a continuous speech database. Our experimental results show that the method allows obtaining average word hypotheses recognition rate of 82%