Power-normalized PLP (PNPLP) feature for robust speech recognition

Author

Lichun Fan ; Dengfeng Ke ; Xiaoyin Fu ; Shixiang Lu ; Bo Xu

fYear

2012

fDate

5-8 Dec. 2012

Firstpage

224

Lastpage

228

Abstract

In this paper, we first review several approaches of feature extraction algorithms in robust speech recognition, e.g. Mel frequency cepstral coefficients (MFCC) [1], perceptual linear prediction (PLP) [2] and power-normalized cepstral coefficients (PNCC) [3]. A new feature extraction algorithm for noise robust speech recognition is proposed, in which medium-time processing works as noise suppression module. The details will be described to show that the algorithm is superior. The experimental results prove that our proposed method significantly outperforms state-of-the-art algorithms.

Keywords

speech recognition; MFCC; Mel frequency cepstral coefficients; PLP; PNCC; PNPLP; feature extraction algorithms; noise robust speech recognition; noise suppression module; perceptual linear prediction; power-normalized PLP feature; power-normalized cepstral coefficients; Feature extraction; Mel frequency cepstral coefficient; Noise; Robustness; Speech; Speech recognition; Robust speech recognition; equal-loudness pre-emphasis; medium-time noise suppression; perceptual linear predictive; power normalization;

fLanguage

English

Publisher

ieee

Conference_Titel

Chinese Spoken Language Processing (ISCSLP), 2012 8th International Symposium on

Conference_Location

Kowloon

Print_ISBN

978-1-4673-2506-6

Electronic_ISBN

978-1-4673-2505-9

Type

conf

DOI

10.1109/ISCSLP.2012.6423529

Filename

6423529