Abstract
This paper introduces a novel context-sensitive feature extraction approach for spontaneous speech recognition. As bidirectional Long Short-Term Memory (BLSTM) networks are known to enable improved phoneme recognition accuracies by incorporating long-range contextual information into speech decoding, we integrate the BLSTM principle into a Tandem front-end for probabilistic feature extraction. Unlike previously proposed approaches which exploit BLSTM modeling by generating a discrete phoneme prediction feature, our feature extractor merges continuous high-level probabilistic BLSTM features with low-level features. Evaluations on challenging spontaneous, conversational speech recognition tasks show that this concept prevails over recently published architectures for feature-level context modeling.
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