PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multi Channel Sequence Processing
Samy Bengio and Hervé Bourlard
In: Machine Learning Workshop, 2005, Sheffield, UK.

Abstract

This paper summarizes some of the current research challenges arising from multi-channel sequence processing. Indeed, multiple real life applications involve simultaneous recording and analysis of multiple information sources, which may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. Some of these problems can already be tackled by one of the many statistical approaches towards sequence modeling. However, several challenging research issues are still open, such as taking into account asynchrony and correlation between several feature streams, or handling the underlying growing complexity. In this framework, we discuss here two novel approaches, which recently started to be investigated with success in the context of large multimodal problems. These include the asynchronous HMM, providing a principled approach towards the processing of multiple feature streams, and the layered HMM approach, providing a good formalism for decomposing large and complex (multi-stream) problems into layered architectures. As briefly reported here, combination of these two approaches yielded successful results on several multi-channel tasks, ranging from audio-visual speech recognition to automatic meeting analysis.

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EPrint Type:Conference or Workshop Item (Lecture)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Speech
Multimodal Integration
ID Code:1094
Deposited By:Samy Bengio
Deposited On:26 September 2005