Paper Info
Title
Optimization of Gabor Features for Text-Independent Speaker Identification
Abstract
For text-independent speaker identification a promi- nent combination is to use Gaussian Mixture Models (GMM) for classification while relying on Mel-Frequency Cepstral Co- efficients (MFCC) as features. To take temporal information into account the time difference of features of adjacent speech frames are appended to the initial features. In this paper we investigate the applicability of spectro-temporal features obtained from Gabor-Filters and present an algorithm for optimizing the possible parameters. Simulation results on a database show that spectro-temporal features achieve higher recognition rates than purely temporal features for clean speech as well as for disturbed speech.
Year
DOI
Venue
2007
10.1109/ISCAS.2007.378660
New Orleans, LA
Keywords
Field
DocType
Gabor filters,Gaussian processes,cepstral analysis,speaker recognition,Gabor features,Gabor-filters,Gaussian mixture models,Mel-frequency cepstral coefficients,spectro-temporal features,speech frames,text-independent speaker identification
Mel-frequency cepstrum,Speaker identification,Pattern recognition,Computer science,Speech recognition,Speaker recognition,Gaussian process,Artificial intelligence,Cepstral analysis,Time difference,Mixture model
Conference
ISSN
ISBN
Citations 
0271-4302
1-4244-0921-7
2
PageRank 
References 
Authors
0.49
4
4
Name
Order
Citations
PageRank
Volker Mildner120.49
Stefan Goetze213215.15
Kammeyer, K.-D.319421.42
Alfred Mertins453476.48