By Topic

Speaker identification with wavelet decomposition and neural networks

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)
Phan, F. ; Dept. of Biomed. Eng., Rutgers Univ., Piscataway, NJ, USA ; Micheli-Tzanakou, E. ; Sideman, S.

The “cocktail party” effect describes the phenomena in which humans can selectively focus attention to one sound source among competing sound sources, which is an ability that is hampered for hearing impaired individuals. An off-line system has been developed in which a speaker is successfully identified in the presence of competing speakers for short utterances in which features used for identification are monaural, whose feature space represent a 90% data reduction from the original data. This system has also been applied to intraspeaker speech recognition. Wavelets are used to generate the multiresolution time-frequency features that are used to characterize the speech waveform. ALOPEX is an optimization paradigm that incorporates these features into a pattern recognition system through template matching or connectivity weight updating in a feedforward artificial neural network

Published in:

Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE

Date of Conference:

1994