Learning vector quantization in text-independent automatic speaker recognition

Author

Filho, Thomas E Filgueiras ; Messina, Ronaldo O. ; Cabral, Euvaldo F.

Author_Institution

Escola Politecnica, Sao Paulo Univ., Brazil

fYear

1998

fDate

9-11 Dec 1998

Firstpage

135

Lastpage

139

Abstract

In this paper is reported a comparison among the learning vector quantization (LVQ) and two other common approaches to text-independent speaker recognition, namely Gaussian mixture models (GMM) and vector quantization (VQ). The LVQ method uses neural nets. The results shows that it is less efficient in terms of recognition scores than the GMM

Keywords

learning (artificial intelligence); neural nets; speaker recognition; vector quantisation; GMM; Gaussian mixture models; LVQ; VQ; learning vector quantization; neural nets; text-independent automatic speaker recognition; Argon; Automatic speech recognition; Electronic learning; Electronic switching systems; Speaker recognition; Speech recognition; Statistical analysis; Testing; Text recognition; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1998. Proceedings. Vth Brazilian Symposium on

Conference_Location

Belo Horizonte

Print_ISBN

0-8186-8629-4

Type

conf

DOI

10.1109/SBRN.1998.731010

Filename

731010

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2351252