Online environmental adaptation of CNN-based acoustic models using spatial diffuseness features

Conference contribution


Publication Details

Author(s): Hümmer C, Delcroix M, Ogawa A, Kinoshita K, Nakatani T, Kellermann W
Publication year: 2017
Pages range: 4875-4879
ISBN: 978-1-5090-4117-6
Language: English


Abstract

We propose a new concept for adapting CNN-based acoustic models using
spatial diffuseness features as auxiliary information about the acoustic
environment: the spatial diffuseness features are simultaneously
employed as acoustic-model input features and to estimate environmental
cues for context adaptation, where one convolutional layer is factorized
into several sub-layers to represent different acoustic conditions.
This context-adaptive CNN-based acoustic model facilitates an online
environmental adaptation and is experimentally verified for the
real-world recordings provided by the CHiME-3 task. The best performing
setup reduces the average word error rate scores achieved by the
baseline system (without using spatial diffuseness features) from 19.4%
to 15.9% and 12.2% to 10.7% considering two experimental setups with and
without front-end signal enhancement, respectively.


FAU Authors / FAU Editors

Hümmer, Christian
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik


How to cite

APA:
Hümmer, C., Delcroix, M., Ogawa, A., Kinoshita, K., Nakatani, T., & Kellermann, W. (2017). Online environmental adaptation of CNN-based acoustic models using spatial diffuseness features. (pp. 4875-4879). New Orleans, US.

MLA:
Hümmer, Christian, et al. "Online environmental adaptation of CNN-based acoustic models using spatial diffuseness features." Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans 2017. 4875-4879.

BibTeX: 

Last updated on 2018-16-10 at 15:08