Cart (Loading....) | Create Account
Close category search window

Dealing with acoustic mismatch for training multilingual subspace Gaussian mixture models for speech 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
$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

3 Author(s)
Mohan, A. ; Dept. of Electr. & Comput. Eng., McGill Univ., Montreal, QC, Canada ; Ghalehjegh, S.H. ; Rose, R.C.

The subspace Gaussian mixture model (SGMM) has been recently proposed as an acoustic modeling technique suitable for configuring multilingual speech recognition systems. It is attractive for this purpose since its parametrization allows its “shared” model parameters to be trained with data from multiple languages [1]. In this work, we report on the results of an experimental study carried out with the goal of improving native Spanish language speech recognition performance using an existing telephone speech corpus of English spoken by speakers of Spanish origin. Compensation for sources of acoustic variability between Spanish and English language data sets was found to be important in obtaining good multilingual ASR performance. We conclude with a discussion about the notion of acoustic similarity between the state dependent parameters of the SGMM, and its possible use in effectively modelling pronunciation variation.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.