In this work we test the performance of RASTA-style modulation filters derived under reverberant conditions. The modulation filters are constructed through linear discriminant analysis of log critical band energies in a manner described by van Vuuren and Hermansky (1997). In previous work we had observed the properties of the resultant filters under a number of acoustic conditions that were artificially applied to the training speech. Here, we present automatic speech recognition results that compare the performance of these filters under some training and testing reverberant conditions. We also test the effectiveness and robustness of a multi-stream combination using probability streams trained under different reverberant environments. The experiments reveal some performance improvement in severe reverberation.
['Michael L. Shire', 'Barry Y. Chen']
Data-driven RASTA filters in reverberation