NDLI: On-line adaptation of semantic models for spoken language understanding

Content Provider	IEEE Xplore Digital Library
Author	Bayer, A.O. Riccardi, G.
Copyright Year	2013
Description	Author affiliation: Signals & Interactive Syst. Lab., Univ. of Trento, Trento, Italy (Bayer, A.O.; Riccardi, G.)
Abstract	Spoken language understanding (SLU) systems extract semantic information from speech signals, which is usually mapped onto concept sequences. The distribution of concepts in dialogues are usually sparse. Therefore, general models may fail to model the concept distribution for a dialogue and semantic models can benefit from adaptation. In this paper, we present an instance-based approach for on-line adaptation of semantic models. We show that we can improve the performance of an SLU system on an utterance, by retrieving relevant instances from the training data and using them for on-line adapting the semantic models. The instance-based adaptation scheme uses two different similarity metrics edit distance and n-gram match score on three different to-kenizations; word-concept pairs, words, and concepts. We have achieved a significant improvement (6% relative) in the understanding performance by conducting rescoring experiments on the n-best lists that the SLU outputs. We have also applied a two-level adaptation scheme, where adaptation is first applied to the automatic speech recognizer (ASR) and then to the SLU.
Starting Page	90
Ending Page	95
File Size	277860
Page Count	6
File Format	PDF
ISBN	9781479927562
DOI	10.1109/ASRU.2013.6707711
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-08
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Training data Hidden Markov models Training Semantics Data models Upper bound On-line Adaptation Spoken Language Understanding Recurrent Neural Networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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