Paper Info
Title
Target-Oriented Phone Selection From Universal Phone Set For Spoken Language Recognition
Abstract
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.
Year
Venue
Keywords
2008
INTERSPEECH 2008: 9TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2008, VOLS 1-5
target-oriented phone tokenizer, spoken language recognition, parallel phone recognizer, vector space modeling, universal phone recognizer
Field
DocType
Citations 
Computer science,Speech recognition,Phone,NIST,Natural language processing,Artificial intelligence,Vector space model,Discriminative model,Spoken language
Conference
2
PageRank 
References 
Authors
0.37
12
4
Name
Order
Citations
PageRank
Rong Tong110811.33
Bin Ma260047.26
Haizhou Li33678334.61
Eng Siong Chng4970106.33