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Paper Detail

Presentation #	2
Session:	Spoken Language Understanding
Session Time:	Wednesday, December 19, 10:00 - 12:00
Presentation Time:	Wednesday, December 19, 10:00 - 12:00
Presentation:	Poster
Topic:	Spoken language understanding:
Paper Title:	RANKING APPROACH TO COMPACT TEXT REPRESENTATION FOR PERSONAL DIGITAL ASSISTANTS
Authors:	Issac Alphonso; Microsoft
	Nick Kibre; Microsoft
	Tasos Anastasakos; Microsoft
Abstract:	Personal digital assistants must display the output from the speech recognizer in a compact and readable representation. The process of transforming sequences from spoken words to written text is called inverse text normalization (ITN). In this paper, we present a ranking based approach to ITN that incorporates predicative information from various neural-net LSTM and n-gram models to select the best written text to display. Our approach ranks the written text candidates, generated by applying weighted FSTs to the spoken words, using a gradient boosted decision tree ensemble (GBDT). The ranker achieves a 18.48% relative reduction in word error rate over an unweighted FST system. Further, our two-stage approach allows us to decouple speech recognition from ITN and gives us greater flexibility in system configuration, since the written-form can vary by domain.