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Hierarchical Pitman-Yor Language Models for ASR in Meetings

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Original languageEnglish
Title of host publicationAutomatic Speech Recognition and Understanding, 2007
Subtitle of host publicationASRU. IEEE Workshop on
Place of PublicationKyoto, Japan
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages124-129
Number of pages6
ISBN (Electronic)978-1-4244-1746-9
ISBN (Print)978-1-4244-1746-9
DOIs
Publication statusPublished - 1 Dec 2007
EventIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU'07) - Kyoto, Japan
Duration: 9 Dec 200713 Dec 2007

Workshop

WorkshopIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU'07)
CountryJapan
CityKyoto
Period9/12/0713/12/07

Abstract

In this paper we investigate the application of a novel technique for language modeling - a hierarchical Bayesian language model (LM) based on the Pitman-Yor process - on automatic speech recognition (ASR) for multiparty meetings. The hierarchical Pitman-Yor language model (HPYLM), which was originally proposed in the machine learning field, provides a Bayesian interpretation to language modeling. An approximation to the HPYLM recovers the exact formulation of the interpolated Kneser-Ney smoothing method in n-gram models. This paper focuses on the application and scalability of HPYLM on a practical large vocabulary ASR system. Experimental results on NIST RT06s evaluation meeting data verify that HPYLM is a competitive and promising language modeling technique, which consistently performs better than interpolated Kneser-Ney and modified Kneser-Ney n-gram LMs in terms of both perplexity (PPL) and word error rate (WER).

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