Improved MFCC feature extraction by PCA-optimized filter-bank for speech recognition
Resource
Automatic Speech Recognition and Understanding, 2001. ASRU '01. IEEE Workshop on
Journal
IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01
Pages
49-52
Date Issued
2001-12
Date
2001-12
Author(s)
DOI
N/A
Abstract
Although Mel-frequency cepstral coefficients (MFCC) have been proven to perform very well under most conditions, some limited efforts have been made in optimizing the shape of the filters in the filter-bank in the conventional MFCC approach. This paper presents a new feature extraction approach that designs the shapes of the filters in the filter-bank. In this new approach, the filter-bank coefficients are data-driven and obtained by applying principal component analysis (PCA) to the FFT spectrum of the training data. The experimental results show that this method is robust under noisy environment and is well additive with other noise-handling techniques.
SDGs
Type
journal article
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