title: Phonetically trained models for speaker recognition.
abstract: In this paper, a speaker recognition system that introduces acoustic information into a Gaussian mixture model (GMM)-based recognizer is presented. This is achieved by using a phonetic classifier during the training phase. The experimental results show that, while maintaining the recognition rate, the decrease in the computational load is between 65% and 80% depending on the number of mixtures of the models.
authors.affiliation: Departamento de Tecnoloxías das Communicacións, ETSE Telecommunicación, Universidade de Vigo, Spain. leandro@gts.tsc.uvigo.es
authors.forename: L
authors.initials: L
authors.lastname: Rodríguez-Liñares
authors.forename: C
authors.initials: C
authors.lastname: Garciá-Mateo
mesh_terms: D000328:Adult; D000465:Algorithms; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D010700:Phonetics; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013018:Sound Spectrography / Q000295:instrumentation*; D013061:Speech Acoustics