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Decoding-Time Prediction of Non-Verbalized Punctuation

Anoop Deoras and Jurgen Fritsch


This paper presents novel methods that integrate lexical prediction of non-verbalized punctuations with Viterbi decoding for Large Vocabulary Conversational Speech Recognition (LVCSR) in a single pass. We describe two different approaches - one based on a modified finite state machine representation of language models and one based on an extension of an LVCSR decoder. We discuss advantages over traditional punctuation prediction approaches based on post-processing of recognition hypotheses, including experimental evaluation of the proposed approach using a state-of-the-art LVCSR decoder. Experiments were performed on a medical documentation corpus and results demonstrate that the proposed methods yield improved punctuation prediction accuracy while at the same time reducing system complexity and memory requirements.


Publication typeInproceedings
Published inISCA Interspeech
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