The traditional approach to automatic speech recognition continues to push the limits of its implementation. The multimodal approach to audio-visual speech recognition and its neuromorphic computational modeling is a novel data driven paradigm that will lead towards zero instruction set computing and will enable proactive capabilities in audio-visual recognition systems. An engineering-oriented deployment of the audio-visual processing framework is discussed in this paper, proposing a bimodal speech recognition framework to process speech utterances and lip reading data, applying soft computing paradigms according to a bio-inspired and the holistic modeling of speech

Bio-inspired audio-visual speech recognition towards the zero instruction set computing / M. Malcangi, H. Quan (COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE). - In: Engineering Applications of Neural Networks / [a cura di] C. Jayne, L. Iliadis. - Prima edizione. - Switzerland : Springer, 2016. - ISBN 9783319441870. - pp. 326-334 (( Intervento presentato al 17. convegno EANN tenutosi a Aberdeen nel 2016 [10.1007/978-3-319-44188-7_25].

Bio-inspired audio-visual speech recognition towards the zero instruction set computing

M. Malcangi;
2016

Abstract

The traditional approach to automatic speech recognition continues to push the limits of its implementation. The multimodal approach to audio-visual speech recognition and its neuromorphic computational modeling is a novel data driven paradigm that will lead towards zero instruction set computing and will enable proactive capabilities in audio-visual recognition systems. An engineering-oriented deployment of the audio-visual processing framework is discussed in this paper, proposing a bimodal speech recognition framework to process speech utterances and lip reading data, applying soft computing paradigms according to a bio-inspired and the holistic modeling of speech
Audio-visual information processing; Automatic speech recognition; Bio-inspired computing; Convolutional neural networks; Evolving fuzzy neural networks
Settore INF/01 - Informatica
SICSA INNS Springer RGU EU The Scottish Government
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/438228
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