Document Type

Article

Language

eng

Publication Date

4-13-2012

Publisher

Springer

Source Publication

EURASIP Journal on Audio, Speech, and Music Processing

Source ISSN

1687-4722

Original Item ID

doi: 10.1186/1687-4722-2012-15

Abstract

An increasing number of multilingual applications require language recognition (LRE) as a frontend, but desire low additional computational cost. This article demonstrates a novel architecture for embedding phone based language recognition into a large vocabulary continuous speech recognition (LVCSR) decoder by sharing the same decoding process but generating separate lattices. To compensate for the prior bias introduced by the pronunciation dictionary and the language model of the LVCSR decoder, three different phone lattice reconstruction algorithms are proposed. The underlying goals of these algorithms are to override pronunciation and grammar restrictions to provide richer phonetic information. All of the new algorithms incorporate a vector space modeling backend for improved LRE accuracy. Evaluated on a Mandarin/English detection task, the proposed integrated LVCSR-LRE system using frame-expanded N-best phone lattice achieves comparable performance to a state-of-the-art phone recognition-vector space modeling (PRVSM) system, but with an added computational cost three times lower than that of a separate PRVSM system.

Comments

Published version. EURASIP Journal on Audio, Speech, and Music Processing, Vol. 2012, No. 15 (April 13, 2012). DOI. Published under Creative Commons Attribution License 2.0.

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