Persian Accents Identification Using an Adaptive Neural Network

Author

Rabiee, Azam ; Setayeshi, Saeed

Author_Institution

Comput. Group, IAU - Dolatabad Branch, Esfahan, Iran

Volume

1

fYear

2010

fDate

6-7 March 2010

Firstpage

7

Lastpage

10

Abstract

Speaker´s accent can reduce the performance of automatic speech recognition systems. This paper considered Persian accent identification using a model includes preprocessing, feature extraction and neural networks. Samples are from five different Persian accents. The performance of the neural networks as an adaptive approach is compared with two statistical approaches. The effect of increasing the number of accents in performance is shown here too.

Keywords

feature extraction; natural language processing; neural nets; speaker recognition; Persian accents identification; adaptive neural network; automatic speech recognition systems; feature extraction; speaker accent; Adaptive systems; Artificial neural networks; Automatic speech recognition; Biological system modeling; Computer science; Educational technology; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Neural networks; accent identification; artificial neural network; feature extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Education Technology and Computer Science (ETCS), 2010 Second International Workshop on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6388-6

Electronic_ISBN

978-1-4244-6389-3

Type

conf

DOI

10.1109/ETCS.2010.273

Filename

5460183