Detail publikace

Deriving Spectro-temporal Properties of Hearing from Speech Data

ONDEL YANG, L. LI, R. SELL, G. HEŘMANSKÝ, H.

Originální název

Deriving Spectro-temporal Properties of Hearing from Speech Data

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

perception, spectro-temporal, auditory, deep learning

Autoři

ONDEL YANG, L.; LI, R.; SELL, G.; HEŘMANSKÝ, H.

Vydáno

12. 5. 2019

Nakladatel

IEEE Signal Processing Society

Místo

Brighton

ISBN

978-1-5386-4658-8

Kniha

Proceedings of ICASSP

Strany od

411

Strany do

415

Strany počet

5

URL

https://ieeexplore.ieee.org/document/8682787

BibTex

@inproceedings{BUT160004,
  author="ONDEL YANG, L. and LI, R. and SELL, G. and HEŘMANSKÝ, H.",
  title="Deriving Spectro-temporal Properties of Hearing from Speech Data",
  booktitle="Proceedings of ICASSP",
  year="2019",
  pages="411--415",
  publisher="IEEE Signal Processing Society",
  address="Brighton",
  doi="10.1109/ICASSP.2019.8682787",
  isbn="978-1-5386-4658-8",
  url="https://ieeexplore.ieee.org/document/8682787"
}