Paper Info
Title
Vtln Warping Factor Estimation Using Accumulation Of Sufficient Statistics
Abstract
In this paper we present an efficient and flexible approach to VTLN warping factor estimation. Due to the equivalence of frequency warping and linear transformation of cepstral coefficients, warping factors can be efficiently estimated by accumulating the sufficient statistics for linear transformation estimation, and searching the constrained space of transformations given by the explicit mapping between warping factors and linear transformation matrices. We show that the positive effect of using a properly normalized optimization criterion for warping factor estimation, which has been previously demonstrated for a signal analysis front-end without a filter-bank, carries over to a MFCC front-end, resulting in a net improvement in word error rate.
Year
DOI
Venue
2006
10.1109/ICASSP.2006.1660242
2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13
Keywords
Field
DocType
speech recognition,mel frequency cepstral coefficient,word error rate,statistics,automatic speech recognition,linear transformation,front end,signal analysis,sufficient statistic,filter bank,cepstrum
Signal processing,Mel-frequency cepstrum,Image warping,Normalization (statistics),Pattern recognition,Matrix (mathematics),Word error rate,Linear map,Artificial intelligence,Sufficient statistic,Mathematics
Conference
ISSN
Citations 
PageRank 
1520-6149
3
0.41
References 
Authors
7
3
Name
Order
Citations
PageRank
Jonas Lööf1815.81
Hermann Ney2141781506.93
Srinivasan Umesh39316.31