NDLI: Neural network models of sensory integration for improved vowel recognition

Content Provider	IEEE Xplore Digital Library
Author	Yuhas, B.P. Goldstein Jr., M.H. Sejnowski, T.J. Jenkins, R.E.
Copyright Year	1963
Abstract	It is demonstrated that multiple sources of speech information can be integrated at a subsymbolic level to improve vowel recognition. Feedforward and recurrent neural networks are trained to estimate the acoustic characteristics of a vocal tract from images of the speaker's mouth. These estimates are then combined with the noise-degraded acoustic information, effectively increasing the signal-to-noise ratio and improving the recognition of these noise-degraded signals. Alternative symbolic strategies such as direct categorization of the visual signals into vowels are also presented. The performances of these neural networks compare favorably with human performance and with other pattern-matching and estimation techniques.<>
Sponsorship	IEEE Publication
Starting Page	1658
Ending Page	1668
Page Count	11
File Size	1314933
File Format	PDF
ISSN	00189219
Volume Number	78
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1990-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Speech recognition Acoustic noise Signal to noise ratio Automatic speech recognition Speech enhancement Humans Degradation Mouth Working environment noise
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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