5th International Conference on Spoken Language Processing
This paper presents a neural network inspired approach to speaker recognition using speaker models constructed from full data sets. A similarity measure between data sets is used for text-independent speaker identification and verification. In order to reduce the computational effort in calculating the similarity measure, a fuzzy Vector Quantisation procedure is applied. This method has previously been successfully applied to a database of 108 Australian English speakers. The purpose of this paper is to apply this method to a larger benchmark database of 630 speakers (TIMIT Database). Using the full 630-speaker database, an accuracy of 98.2% (one test sentence) and 99.7% (two test sentences) was achieved for text-independent speaker identification. On a 462-speaker subset of the database a 98.5% successful acceptance and 96.9% successful rejection rate for text-independent speaker verification was achieved.
Bibliographic reference. Ward, Nuala C. / Dersch, Dominik R. (1998): "Text-independent speaker identification and verification using the TIMIT database", In ICSLP-1998, paper 0291.