Paper Info
Title
A Lattice-Based Phonotactic Language Recognition System with CMLLR Adaptation and Its Implementation Issues
Abstract
This paper presents a ¿non-complicated¿ automatic spoken language recognition system which can be effectively implemented using publicly available toolkits (such as HTK, SRILM and SVM-Light) and corpus resources (such as Switchboard, CallFriend, OHSU and NIST LRE07 speech corpora). This system involves two context-independent phone recognizers, a vector space modelling classifier and an equal weight fusion of likelihood scores from the classifier. CMLLR adaptation and phone lattice are also used in this system. Our experiments show that these two techniques are essential in obvious performance improvement. Despite the simplicity of the system, it achieves the EER of 2.72% in the 30-sec condition in NIST LRE-2007 evaluation data set. Moreover, we describe our experience how we use the large amount of available training data to effectively test different configurations in the phone recognizers. This practical issue should be interesting to the later comers who plan to participate in NIST Language Recognition evaluation or similar international benchmark campaigns.
Year
DOI
Venue
2009
10.1109/IALP.2009.67
IALP
Keywords
Field
DocType
corpus resources,lattice-based phonotactic language recognition,available training data,speech processing,nist lre-2007 evaluation data,speech recognition,cmllr adaptation,nist language recognition,30-sec condition,natural languages,vector space modelling classifier,phone lattice,automatic spoken language recognition system,implementation issues,lattice based phonotactic language recognition system,spoken language recognition,nist lre07 speech corpus,phone recognizer,phone recognizers,language recognition system,available toolkits,context-independent phone recognizers,weight fusion,nist language recognition evaluation,vectors,lattices,hidden markov models,vector space,speech,decoding,training data
Speech processing,Computer science,Speech recognition,NIST,Phone,Natural language,Natural language processing,Artificial intelligence,Classifier (linguistics),Hidden Markov model,Spoken language,Performance improvement
Conference
ISSN
ISBN
Citations 
2159-1962
978-0-7695-3904-1
4
PageRank 
References 
Authors
0.58
9
4
Name
Order
Citations
PageRank
Cheung-Chi Leung124425.37
Rong Tong210811.33
Bin Ma360047.26
Haizhou Li43678334.61