Title :
Phoneme-less hierarchical accent classification
Author :
Lin, Xiaofan ; Simske, Steven
Author_Institution :
Hewlett-Packard Co., Palo Alto, CA, USA
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;
Conference_Titel :
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
Print_ISBN :
0-7803-8622-1
DOI :
10.1109/ACSSC.2004.1399473