We propose a novel approach to design modulation frequency filters for the first stage processing of critical band spectrum of speech using deep neural network (DNN). These filters replace conventional modulation frequency filters currently used in state-of-the-art BUT speech recognition system and yield about 10% relative improvement in phoneme recognition accuracy. The resulting filters are consistent with some known temporal properties of higher levels of mammalian auditory processing and suggest more efficient scheme for pre-processing of speech for ASR.
Cite as: Pešán, J., Burget, L., Hermansky, H., Veselý, K. (2015) DNN derived filters for processing of modulation spectrum of speech. Proc. Interspeech 2015, 1908-1911, doi: 10.21437/Interspeech.2015-421
@inproceedings{pesan15_interspeech,
author={Jan Pešán and Lukáš Burget and Hynek Hermansky and Karel Veselý},
title={{DNN derived filters for processing of modulation spectrum of speech}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={1908--1911},
doi={10.21437/Interspeech.2015-421}
}