By Topic

Fast speaker adaptation of large vocabulary continuous density HMM speech recognizer using a basis transform approach

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

2 Author(s)
Boulis, C. ; Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece ; Digalakis, V.

Maximum likelihood transformation-adaptation techniques have proven successful, but it is believed that faster convergence to speaker dependent (SD) performance can be achieved if we incorporate some form of a-priori knowledge in the adaptation process. In this paper, instead of estimating one linear transform per class of models for each new speaker, we transform the speaker-independent (SI) models using multiple linear transforms and a weight vector. To reduce the number of adaptation parameters, the multiple linear transforms are generated from training speakers and the adaptation parameters consist of a single weight vector per class. This can be seen as incorporating a-priori knowledge to our estimation process. Experiments conducted on the Spoken Language Translator database in the Swedish language using SRI's DECIPHERTM system, show that the new method outperforms maximum likelihood linear regression on very limited adaptation data

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:2 )

Date of Conference: