Authors:
Chi-Geun Lee
;
Mun-Sung Han
;
Chang-Seok Bae
and
Jin-Tae Kim
Affiliation:
Electronic and Telecommunication Research Institute, Korea, Republic of
Keyword(s):
Probability Density Function (PDF), Multi-modal recognition, Classifier level Fusion Recognition, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Recently, multi-modal recognition has become a hot topic in the field of Ubiquitous, Speech and gesture recognition, especially, are the most important modalities of human-to-machine interaction. Although speech recognition has been explored extensively and successfully developed, it still encounters serious errors in noisy environments. In such cases, gestures, a by-product of speech, can be used to help interpret the speech. In this paper, we propose a method of multi-modal fusion recognition of speech-gesture using integrated discrete probability density function omit estimated by a histogram. The method is tested with a microphone and a 3-axis accelerator in a real-time experiment. The test has two parts : a method of add-and-accumulate speech and gesture probability density functions respectively, and a more complicated method of creating new probability density function from integrating the two PDF’s of speech and gesture.