Target-oriented phone selection from universal phone set for spoken language recognition

This paper studies target-oriented phone selection strategy for constructing phone tokenizers in the Parallel Phone Recognizers followed by Vector Space Model (PPR-VSM) paradigm of spoken language recognition. With this phone selection strategy, one derives a set of target-oriented phone tokenizers (TOPT), each having a subset of phones that have high discriminative ability for a target language. Two phone selection methods are proposed to derive such phone subsets from a phone recognizer. We show that the TOPTs derived from a universal phone recognizer (UPR) outperform those derived from language specific phone recognizers. The TOPT front-end derived from a UPR also consistently outperforms the UPR front-end without involving additional acoustic modeling. We achieve an equal error rates (EERs) of 1.33%, 1.75% and 2.80% on NIST 1996, 2003 and 2007 LRE databases respectively for 30 second closed-set tests by including multiple TOPTs in the PPR.

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