Paper Info
Title
Reliable accent specific unit generation with dynamic Gaussian mixture selection for multi-accent speech recognition
Abstract
Multiple accents are often present in Mandarin speech, as most Chinese have learned Mandarin as a second language. We propose generating reliable accent specific unit together with dynamic Gaussian mixture selection for multi-accent speech recognition. Time alignment phoneme recognition is used to generate such unit and to model accent variations explicitly and accurately. Dynamic Gaussian mixture selection scheme builds a dynamical observation density for each specified frame in decoding, and leads to use Gaussian mixture component efficiently. This method increases the covering ability for a diversity of accent variations in multi-accent, and alleviates the performance degradation caused by pruned beam search without augmenting the model size. The effectiveness of this approach is evaluated on three typical Chinese accents Chuan, Yue and Wu. Our approach outperforms traditional acoustic model reconstruction approach significantly by 6.30%, 4.93% and 5.53%, respectively on Syllable Error Rate (SER) reduction, without degrading on standard speech.
Year
DOI
Venue
2011
10.1109/ICME.2011.6011941
ICME
Keywords
Field
DocType
specific unit generation,accent variation,multi-accent speech recognition,gaussian mixture component,multiple accent,mandarin speech,reliable accent,dynamic gaussian mixture selection,model accent variation,standard speech,model size,decoding,speech recognition,speech,hidden markov models,reliability,acoustics
Pattern recognition,Computer science,Word error rate,Beam search,Speech recognition,Gaussian,Artificial intelligence,Syllable,Decoding methods,Hidden Markov model,Mandarin Chinese,Acoustic model
Conference
ISSN
Citations 
PageRank 
1945-7871
2
0.41
References 
Authors
6
6
Name
Order
Citations
PageRank
Chao Zhang120.41
Yi Liu2134.43
Yunqing Xia369944.11
Thomas Fang Zheng468992.78
Jesper Olsen5112.44
Jilei Tian658435.38