Feature normalization using MVAW processing for spoken language recognition

Author

Chien-Lin Huang ; Matsuda, Shodai ; Hori, Chiori

Author_Institution

Nat. Inst. of Inf. & Commun. Technol., Kyoto, Japan

fYear

2013

fDate

Oct. 29 2013-Nov. 1 2013

Firstpage

1

Lastpage

4

Abstract

This study presents a noise robust front-end postprocessing technology. After cepstral feature analysis, the feature normalization is usually applied for noisy reduction in spoken language recognition. We investigate a highly effective MVAW processing based on standard MFCC and SDC features on NIST-LRE 2007 tasks. The procedure includes mean subtraction, variance normalization, auto-regression moving-average filtering and feature warping. Experiments were conducted on a common GMM-UBM system. The results indicated significant improvements in recognition accuracy.

Keywords

autoregressive moving average processes; cepstral analysis; feature extraction; filtering theory; speech recognition; GMM-UBM system; MFCC features; MVAW processing; NIST-LRE 2007 task; SDC features; autoregression moving-average filtering; cepstral feature analysis; feature normalization; feature warping; mean subtraction; noise robust front-end postprocessing technology; noisy reduction; recognition accuracy; spoken language recognition; variance normalization; Filtering; Mel frequency cepstral coefficient; Robustness; Speech; Speech processing; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific

Conference_Location

Kaohsiung

Type

conf

DOI

10.1109/APSIPA.2013.6694104

Filename

6694104