Efficient Acoustic Front-End Processing for Tamil Speech Recognition using Modified GFCC Features
Автор: Vimala. C, V. Radha
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 7 vol.8, 2016 года.
Бесплатный доступ
Giving suitable input and features are always essential to obtain better accuracy in Automatic Speech Recognition (ASR). The type of signal and feature vectors given as an input is highly essential as the pattern matching algorithms strongly depends on these two components. The primary goal of this paper is to propose a suitable Pre-processing and feature extraction techniques for speaker independent speech recognition for Tamil language. The five pass Pre-processing and three types of modified feature extraction techniques are introduced using Gammatone Filtering and Cochleagram Coefficients (GFCC) to achieve better recognition performance. The modified GFCC features using multi taper Yule walker AR power spectrum, combinational features using Formant Frequencies (FF), combined frequency warping and feature normalization techniques using Linear Predictive Coding (LPC) and Cepstral Mean Normalization (CMN) are investigated. The experimental results prove that the proposed techniques have produced high recognition accuracy when compared with the conventional GFCC feature extraction technique.
Gammatone Filter banks, Multi Taper window, Yule Walker AR, Formant Feature extraction, Cepstral Mean Normalization, Tamil Speech Recognition
Короткий адрес: https://sciup.org/15013993
IDR: 15013993
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