H93-1019 98.5 % . With 2.5 s of speech the speaker identification accuracy is 98.3 % . For the
H93-1019 using unsupervised adaptation for speaker identification on the 168 TIMIT speakers had
H93-1019 experimented with this approach for speaker identification . In our implementation , each
H93-1019 of sentences longer than 3s , speaker identification was correct , suggesting that
H93-1019 Table 5 , for text-independent speaker identification . As for sex and language identification
H90-1103 Speaker Verification ( SV ) 2.2 Speaker Identification ( SI ) 2.3 Language Identification
H93-1013 speaker-dependent acoustic models for speaker identification . The authors report good performance
H93-1019 effective for language , sex , and speaker identification and can enable better and more
H93-1019 text-independent language , sex , and speaker identification and can enable better and more
H01-1051 nonlexical information such as speaker identification and characterization , voice
H93-1019 phonetic transcription for accurate speaker identification . Being independent of the spoken
H93-1019 used for identification , the speaker identification accuracy is 100 % . This experimental
H93-1019 20 , 27 , 14 \ -RSB- with high speaker identification rates reported using subsets
H93-1019 been used in a few studies on speaker identification \ -LSB- l , 20 , 27 , 14 \ -RSB-
H93-1019 for accuracte text-independent speaker identification . ( EOS is End Of Sentence identification
H93-1019 . Performing text-independent speaker identification as before on the remaining 2
A97-1007 each turn , we store e.g. the speaker identification , the language of the contribution
H93-1013 for language identification , speaker identification , speakersex identification ,
H93-1019 Experiments for text-dependent speaker identification using exactly the same models
D14-1051 identification ) . 4.2 From i-vector speaker identification to c-vector textual document
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