Adaptive speaker identification using sequential probability ratio test

In speaker recognition there are usually a small number of speakers whose utterances are difficult to be recognized correctly and then recognition errors are mainly come from those speakers. Recognition performance for those speakers may be improved if they are urged to produce more utterances. Such speaker-dependent utterance-length control has recently been realized in speaker verification (SV) using the sequential probability ratio test (SPRT). The SPRT is in principle for two-class classification and therefore it Mas naturally applied to SV This paper implements the speaker-dependent utterance-length control using the SPRT first in speaker identification (SI), by making it applicable to multi-class classification. Experimental results show that the proposed SI method is superior on computation time as we[I as error rate, to a conventional method with fixed-length utterances.