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Web derived pronunciations for spoken term detection
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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval table of contents
Boston, MA, USA
SESSION: Speech and linguistic processing table of contents
Pages 83-90  
Year of Publication: 2009
ISBN:978-1-60558-483-6
Authors
Dogan Can  Bogazici University, Istanbul, Turkey
Erica Cooper  MIT, Cambridge, MA, USA
Arnab Ghoshal  Johns Hopkins University, Baltimore, MD, USA
Martin Jansche  Google, Inc., NY, NY, USA
Sanjeev Khudanpur  Johns Hopkins University, Baltimore, MD, USA
Bhuvana Ramabhadran  IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
Michael Riley  Google, Inc., New York, NY, USA
Murat Saraclar  Bogazici University, Istanbul, Turkey
Abhinav Sethy  IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
Morgan Ulinski  Cornell University, Ithaca, NY, USA
Christopher White  Johns Hopkins University, Baltimore, MD, USA
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Indexing and retrieval of speech content in various forms such as broadcast news, customer care data and on-line media has gained a lot of interest for a wide range of applications, from customer analytics to on-line media search. For most retrieval applications, the speech content is typically first converted to a lexical or phonetic representation using automatic speech recognition (ASR). The first step in searching through indexes built on these representations is the generation of pronunciations for named entities and foreign language query terms. This paper summarizes the results of the work conducted during the 2008 JHU Summer Workshop by the Multilingual Spoken Term Detection team, on mining the web for pronunciations and analyzing their impact on spoken term detection. We will first present methods to use the vast amount of pronunciation information available on the Web, in the form of IPA and ad-hoc transcriptions. We describe techniques for extracting candidate pronunciations from Web pages and associating them with orthographic words, filtering out poorly extracted pronunciations, normalizing IPA pronunciations to better conform to a common transcription standard, and generating phonemic representations from ad-hoc transcriptions. We then present an analysis of the effectiveness of using these pronunciations to represent Out-Of-Vocabulary (OOV) query terms on the performance of a spoken term detection (STD) system. We will provide comparisons of Web pronunciations against automated techniques for pronunciation generation as well as pronunciations generated by human experts. Our results cover a range of speech indexes based on lattices, confusion networks and one-best transcriptions at both word and word fragments levels.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Dogan Can: colleagues
Erica Cooper: colleagues
Arnab Ghoshal: colleagues
Martin Jansche: colleagues
Sanjeev Khudanpur: colleagues
Bhuvana Ramabhadran: colleagues
Michael Riley: colleagues
Murat Saraclar: colleagues
Abhinav Sethy: colleagues
Morgan Ulinski: colleagues
Christopher White: colleagues