By Topic

A VQ-based preprocessor using cepstral dynamic features for speaker-independent large vocabulary word recognition

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
$33 $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

1 Author(s)
Furui, S. ; Human Interface Lab., NTT, Tokyo, Japan

A VQ (vector quantization)-based preprocessor is proposed which reduces the amount of computation in speaker-independent large-vocabulary isolated-word recognition. The features introduced here are the use of a universal codebook in the VQ-based preprocessor and the use of multiple feature sets including cepstral dynamic features. Word-specific codebooks are used for front-end preprocessing to eliminate word candidates whose distance scores are large. A dynamic time-warping (DTW) processor based on a word dictionary, in which each word is represented as a time sequence of the universal codebook elements (SPLIT method), then resolves the choice among the remaining word candidates. Recognition experiments using a database consisting of words from a vocabulary of 100 Japanese city names uttered by 20 male speakers confirmed the effectiveness of this method. The total amount of calculation necessary in this condition is almost 1/10 of that without preprocessing

Published in:

Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:36 ,  Issue: 7 )