Hierarchical Pitman-Yor Language Models for ASR in Meetings

Songfang Huang, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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).
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)
Number of pages6
ISBN (Electronic)978-1-4244-1746-9
ISBN (Print)978-1-4244-1746-9
Publication statusPublished - 1 Dec 2007
EventIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU'07) - Kyoto, Japan
Duration: 9 Dec 200713 Dec 2007


WorkshopIEEE Workshop on Automatic Speech Recognition and Understanding (ASRU'07)


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