Associative neural networks for machine consciousness: Improving existing AI technologies

In this research we look at ways for improving existing AI techniques by the use of associative neural networks, proposed by Haikonen for machine consciousness. We find that all examined technologies do profit from such an approach: speech recognition, emotion recognition in speech, EMG data analysi...

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Bibliographic Details
Published in2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel pp. 011 - 015
Main Authors Lesser, E., Schaeps, T., Haikonen, P., Jorgensen, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2008
Subjects
Analytical models
Artificial intelligence
Data analysis
Electromyography
Emotion recognition
Neural networks
Random number generation
Speech analysis
Speech processing
Speech recognition
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Summary:In this research we look at ways for improving existing AI techniques by the use of associative neural networks, proposed by Haikonen for machine consciousness. We find that all examined technologies do profit from such an approach: speech recognition, emotion recognition in speech, EMG data analysis for multilingual speech processing, the simulation of bistable perception and the generation of random numbers. EMG data analysis for multilingual speech processing (silent speech recognition) is selected as the main example in this paper for its simple yet complete architecture. We discuss the development of a test bench and give an overview of results obtained.
ISBN:142442481X
9781424424818
DOI:10.1109/EEEI.2008.4736701