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.

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In  ISCA Interspeech

Publisher  ISCA

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