Hybrid wavelet based LPC features for Hindi speech recognition
by Aditya Sharma, M.C. Shrotriya, Omar Farooq, Z.A. Abbasi
International Journal of Information and Communication Technology (IJICT), Vol. 1, No. 3/4, 2008

Abstract: Hybrid features are presented for speech recognition that uses linear prediction in combination with multi-resolution capabilities of wavelet transform. Wavelet-Based Linear Prediction Coefficients (WBLPC) are obtained by applying 3 and 4-level wavelet decomposition and then having linear prediction of each sub-bands to get total 13 features. These features have been tested using a linear discriminant function and Hidden Markov Model (HMM) based classifier for speaker dependent and independent isolated Hindi digits recognition. 3-level WBLPC features gave higher percentage recognition than LPC features while 4-level WBLPC features using HMM gave the highest percentage recognition for both speaker dependent and independent cases.

Online publication date: Mon, 23-Mar-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information and Communication Technology (IJICT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com