Publication Details

Deriving Spectro-temporal Properties of Hearing from Speech Data

ONDEL Lucas, LI Ruizhi, SELL Gregory and HEŘMANSKÝ Hynek. Deriving Spectro-temporal Properties of Hearing from Speech Data. In: Proceedings of ICASSP. Brighton: IEEE Signal Processing Society, 2019, pp. 411-415. ISBN 978-1-5386-4658-8. Available from: https://ieeexplore.ieee.org/document/8682787
Czech title
Odvozování spektrálně-časových vlastností slyšení z řečových dat
Type
conference paper
Language
english
Authors
Ondel Lucas, Mgr. (DCGM FIT BUT)
Li Ruizhi (JHU)
Sell Gregory (JHU)
Heřmanský Hynek, prof. Ing., Dr.Eng. (JHU)
URL
Keywords
perception, spectro-temporal, auditory, deep learning
Abstract
Human hearing and human speech are intrinsically tied together, as the properties of speech almost certainly developed in order to be heard by human ears. As a result of this connection, it has been shown that certain properties of human hearing are mimicked within data-driven systems that are trained to understand human speech. In this paper, we further explore this phenomenon by measuring the spectro-temporal responses of data-derived filters in a front-end convolutional layer of a deep network trained to classify the phonemes of clean speech. The analyses show that the filters do indeed exhibit spectro-temporal responses similar to those measured in mammals, and also that the filters exhibit an additional level of frequency selectivity, similar to the processing pipeline assumed within the Articulation Index.
Published
2019
Pages
411-415
Proceedings
Proceedings of ICASSP
Conference
International Conference on Acoustics, Speech, and Signal Processing, Brighton, GB
ISBN
978-1-5386-4658-8
Publisher
IEEE Signal Processing Society
Place
Brighton, GB
DOI
BibTeX
@INPROCEEDINGS{FITPUB12097,
   author = "Lucas Ondel and Ruizhi Li and Gregory Sell and Hynek He\v{r}mansk\'{y}",
   title = "Deriving Spectro-temporal Properties of Hearing from Speech Data",
   pages = "411--415",
   booktitle = "Proceedings of ICASSP",
   year = 2019,
   location = "Brighton, GB",
   publisher = "IEEE Signal Processing Society",
   ISBN = "978-1-5386-4658-8",
   doi = "10.1109/ICASSP.2019.8682787",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/12097"
}
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