By Topic

A weighted approach of missing data technique in cepstra domain based on S-function

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Pei Yi ; Dept. of Mathematic Sci., Tsinghua Univ., Beijing, China ; Yubo Ge

The application of Missing Data Technique (MDT) has shown to improve the performance of speech recognition. To apply MDT to cepstral domain, this paper presents a weighted approach to compute the reliability of cepstral feature based on sigmoid function and introduces a weighted distance algorithm. It is deduced that the reliability compensates the Gaussian variance in hidden Markov model (HMM) frame by frame to reduce the mismatch between clean-trained model and corrupted speech. Experimental evaluation using the Aurora2 database demonstrates a distinct digit error rate reduction. The main advantages of the approach are simple system implementation, low computation cost and easy to plug into other robust recognition algorithm.

Published in:

Multimedia Signal Processing (MMSP), 2010 IEEE International Workshop on

Date of Conference:

4-6 Oct. 2010