{"id":163795,"date":"2008-09-01T00:00:00","date_gmt":"2008-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/decoding-time-prediction-of-non-verbalized-punctuation\/"},"modified":"2018-10-16T20:00:33","modified_gmt":"2018-10-17T03:00:33","slug":"decoding-time-prediction-of-non-verbalized-punctuation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/decoding-time-prediction-of-non-verbalized-punctuation\/","title":{"rendered":"Decoding-Time Prediction of Non-Verbalized Punctuation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>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 &#8211; 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\u00a0 accuracy while at the same time reducing system complexity and memory requirements.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 &#8211; one based on a modified finite state machine representation of language models and one based on an extension of an LVCSR decoder. 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