Speech Data Modeling

This project is concerned with speech and speaker variations as it affects signal processing and acoustic modeling:

Signal processing will involve non-linear speaker and channel adaptation by finding a common low dimensional mapping of training data, based on the J-RASTA signal processing approach. Knowledge of the Multi-band Recognition paradigm will be experimented with and exploited.

Variability as a function of global and local speaking rate will be incorporated into the acoustic model. We will exploit discriminant HMM technology developed by Bourlard and Morgan, using transition probabilities that are dependent on acoustics (and in this case rate).


Team Members
Senior Members
Hynek HermanskyCLSP
Herve BourlardFaculte Polytechnique de Mons (BE) / ICSI
Jordan CohenIDA
Nelson MorganICSI
Christophe RisFaculte Polytechnique de Mons (BE)
Graduate Students
Mark OdrawskiCLSP
Nikki MirghaforiICSI
Sangita TibrewalaOregon Graduate Institute

Center for Language and Speech Processing