A modular connectionist architecture for text-independent talker identification

The authors propose a novel model for text-independent talker identification which uses TDNN (time-delay neural network)-extracted features information. This model has been tested on 20 speakers, ten male and ten female, from the TIMIT database using an LPC (linear prediction coding) parameterization. An average identification of 98% has been observed.<
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