Robust Feature Extraction Using Variable Window Function in Autocorrelation Domain for Speech Recognition

Sangho LEE  Jeonghyun HA  Jaekeun HONG  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E92-A   No.11   pp.2917-2921
Publication Date: 2009/11/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E92.A.2917
Print ISSN: 0916-8508
Type of Manuscript: LETTER
Category: Speech and Hearing
variable window,  AMFCC,  speech recognition,  robust feature,  

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This paper presents a new feature extraction method for robust speech recognition based on the autocorrelation mel frequency cepstral coefficients (AMFCCs) and a variable window. While the AMFCC feature extraction method uses the fixed double-dynamic-range (DDR) Hamming window for higher-lag autocorrelation coefficients, which are least affected by noise, the proposed method applies a variable window, depending on the frame energy and periodicity. The performance of the proposed method is verified using an Aurora-2 task, and the results confirm a significantly improved performance under noisy conditions.