Bhuvana Ramabhadran
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Exemplar-Based Processing for Speech Recognition: An Overview
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Bhuvana Ramabhadran
David Nahamoo
Dimitri Kanevsky
Dirk Van Compernolle
Kris Demuynck
Jort F. Gemmeke
Jerome R. Bellegarda
Shiva Sundaram
IEEE Signal Process. Mag., 29 (2012), pp. 98-113
Speech Retrieval
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Timothy J. Hazen
Bhuvana Ramabhadran
Murat Saraçlar
Spoken Language Understanding, John Wiley and Sons, Ltd (2011), pp. 417-446
Web Derived Pronunciations for Spoken Term Detection
Doğan Can
Erica Cooper
Arnab Ghoshal
Martin Jansche
Sanjeev Khudanpur
Bhuvana Ramabhadran
Murat Saraçlar
Abhinav Sethy
Morgan Ulinski
Christopher White
32nd Annual International ACM SIGIR Conference (2009), pp. 83-90
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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.
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