A novel approach to increase the robustness of speaker independent Arabic speech recognition

Author

Shoaib, M. ; Rasheed, F. ; Akhtar, J. ; Awais, M. ; Masud, S. ; Shamai, S.

Author_Institution

Dept. of Comput. Sci., Lahore Univ. of Manage. Sci., Pakistan

fYear

2003

fDate

8-9 Dec. 2003

Firstpage

371

Lastpage

376

Abstract

This work presents a two-tier approach through sequential application of intensity contours and formant tracks for accurate Arabic phoneme identification. The recognition system developed is based on data sets of 40 speakers for each Arabic phonetic sound. As a first step towards recognition of phonemes, the sound is sampled and then preprocessed to get formant frequencies and intensity contours. In order to automate the intensity and formant based feature extraction, a generalized regression neural network has been implemented, trained and validated on 21 input features.

Keywords

feature extraction; learning (artificial intelligence); neural nets; speech processing; speech recognition; Arabic speech recognition; feature extraction; formant frequencies; formant tracks; generalized regression neural network; intensity contours; neural net training; phoneme identification; speaker independent speech recognition; two-tier approach; Application software; Computer science; Equations; Frequency estimation; Linear predictive coding; Neural networks; Resonant frequency; Robustness; Speech analysis; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Multi Topic Conference, 2003. INMIC 2003. 7th International

Print_ISBN

0-7803-8183-1

Type

conf

DOI

10.1109/INMIC.2003.1416753

Filename

1416753