DocumentCode
2652495
Title
Phoneme-less hierarchical accent classification
Author
Lin, Xiaofan ; Simske, Steven
Author_Institution
Hewlett-Packard Co., Palo Alto, CA, USA
Volume
2
fYear
2004
fDate
7-10 Nov. 2004
Firstpage
1801
Abstract
This paper introduces a novel accent classification method. Compared with existing methods, it has two unique features. First, it does not explicitly utilize phoneme information. Second, it is built on top of the gender classification. We have tested the proposed algorithm on datasets that are completely independent of training data. The accuracy of distinguishing American accent and British accent is 83%. We have also compared the accent classification with the gender classification in terms of accuracy and the saturation behavior with respect to length of utterance.
Keywords
signal classification; speech recognition; gender classification; hierarchical accent classification; phoneme information; Cepstral analysis; Classification algorithms; Customer satisfaction; Data mining; Hidden Markov models; Milling machines; Speech recognition; Stochastic processes; Testing; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
Print_ISBN
0-7803-8622-1
Type
conf
DOI
10.1109/ACSSC.2004.1399473
Filename
1399473
Link To Document