Paper Info
Title
Frame selection of interview channel for NIST speaker recognition evaluation
Abstract
In this paper, we study a front-end frame selection approach for the interview channel speaker recognition system. This new approach keeps the high quality speech frames and removes noisy and irrelevant speech frames for speaker modeling. For robust voice activity detection (VAD) under the different types of microphones located in the interview room, we adopt the spectral subtraction algorithm for noise reduction. An energy based frame selection algorithm is first applied to indicate the speech activity at the frame level. To overcome the summed channel effects in the interview condition, a study is conducted to effectively extract the relevant speaker's speech frames based on VAD Tags and ASR transcript Tags provided by NIST. The eigenchannel based GMM-SVM speaker recognition system is used to evaluate the proposed method. The experiments are conducted on the NIST 2008 and NIST 2010 Speaker Recognition Evaluation interview-interview conditions. It demonstrates that the approach provides an efficient way to select high quality speech frames and the relevant speaker's voice in the interview environment for speaker recognition.
Year
DOI
Venue
2010
10.1109/ISCSLP.2010.5684886
ISCSLP
Keywords
Field
DocType
speaker recognition,gmm-svm,speech processing,energy based frame selection algorithm,interview channel speaker recognition system,asr transcript tags,nist,front-end frame selection approach,spectral subtraction algorithm,noise abatement,spectral analysis,interview channel,robust voice activity detection,nist speaker recognition evaluation,speech activity,gmm-svm speaker recognition system,noise reduction,distant microphone,support vector machines,voice activity detection,speech recognition,interviews,speech,front end
Noise reduction,Speech processing,Pattern recognition,Voice activity detection,Computer science,Support vector machine,Selection algorithm,Speech recognition,Speaker recognition,NIST,Speaker diarisation,Artificial intelligence
Conference
ISBN
Citations 
PageRank 
978-1-4244-6244-5
2
0.42
References 
Authors
7
3
Name
Order
Citations
PageRank
Hanwu Sun19814.15
Bin Ma233728.61
Haizhou Li33678334.61