Notice of RetractionSpeaker classification based on high dimension feature vector

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Audio index is an important part of NIST-RT-SD evaluation since 2003. Speaker Diarization is one kind of audio index technology which is marked by different speakers. One essential component of speaker diarization is speaker clustering which is always the pre-processing of speech recognition. The general method is to extract acoustic feature such as LPCC or MFCC and achieve some model such as HMM or GHMM by training these data. Another way is to treat these data as some vectors and choose the distance criterion between two or more classes. The best DER score of NIST-RT-SD evaluation is 8.51% at 2007. We proposed a new spatial feature vector mixed with traditional acoustic feature vector. The spatial feature vector is provided by distributed microphones random arranged during the conference environment. The high-dimension SVM algorithm is utilized to classify the testing mixed feature vector after the training step accomplished. The experiment results show that the mixed feature vector can improve the classifier´s precision under meeting scene.