A robust, segmental method for text independent speaker identification

Author

Gish, Herbert ; Schmidt, Michael ; Mielke, Angela

Author_Institution

BBN Syst. & Technol. Corp., Cambridge, MA, USA

Volume

i

fYear

1994

fDate

19-22 Apr 1994

Abstract

A robust, segmental method for text independent speaker identification is presented. Probability models are created from training data for each of the speakers of interest. The test sessions are then segmented and the statistics from each segment along with the models are used to compute scores for each speaker. Robust methods are described for combining the scores over all segments into one score. This process is carried out for each of the segment statistics which are combined to form a final score. Zero errors are obtained on a subset of the Switchboard corpus consisting of 24 speakers using six 60 second training sessions for each speaker and 97 thirty second tests

Keywords

probability; speaker recognition; statistics; telephony; 30 s; 60 s; Switchboard corpus; probability models; robust segmental method; segment statistics; speaker identification model; telephone; test sessions; text independent speaker identification; training data; Background noise; Contamination; Probability; Robustness; Speech; Statistical analysis; Statistics; Telephony; Testing; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389334

Filename

389334