IEICE Trans - Robust Speech Features Based on LPC Using Weighted Arcsin Transform

Robust Speech Features Based on LPC Using Weighted Arcsin Transform

Wei-Wen HUNG

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E86-D    No.2    pp.340-343
Publication Date: 2003/02/01
Online ISSN:
DOI:
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Speech and Hearing
Keyword:
SNR-dependent arcsin transform,  linear predictive coding,  autocorrelation sequence,  average magnitude difference function,

Full Text: PDF>>

Summary:
To increase the discriminating ability of the speech feature based on linear predictive coding (LPC) and increase its noise robustness, an SNR-dependent arcsin transform is applied to the autocorrelation sequence (ACS) of each analysis frame in a speech signal. Moreover, each component in the ACS is also weighted by the normalized reciprocal of the average magnitude difference function (AMDF) for emphasizing its peak structure. Experimental results for the task of Mandarin digit recognition indicate that the LPC speech feature employing the proposed scheme is more robust than some widely used LPC-based approaches over a wide range of SNR values.