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 engineeringoriented 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.

Biomorphic Modeling of Phoneme Identification and Classification Based on an Evolving Fuzzy-neural Network : From Hardcomputing to Softcomputing / M. Malcangi, H. Quan, P. Grew (PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS). - In: 2017 International Joint Conference on Neural Networks (IJCNN)[s.l] : IEEE, 2017. - ISBN 9781509061822. - pp. 3092-3097 (( convegno International Joint Conference on Neural Networks tenutosi a Ankorage nel 2017 [10.1109/IJCNN.2017.7966241].

Biomorphic Modeling of Phoneme Identification and Classification Based on an Evolving Fuzzy-neural Network : From Hardcomputing to Softcomputing

M. Malcangi;
2017

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 engineeringoriented 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.
Settore INF/01 - Informatica
IEEE INNS (International Neural Networks Society)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/501922
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