Speaking Status Detection from Body Movements Using Transductive Parameter Transfer

Conference paper (2016)

Authors

E. Gedik Pattern Recognition and Bioinformatics -

H.S. Hung Pattern Recognition and Bioinformatics -

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

Transfer learning Wearable sensors Social action recognition

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http://resolver.tudelft.nl/uuid:88fceb8b-5ef7-44bc-8f08-aa43d2129b6b

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Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

We investigate the task of detecting speakers in crowded environments using a single triaxial accelerometer worn around the neck. Similar to the previous studies, by assuming that body movements are indicative of speech, we show experimentally that transductive transfer learning can better model individual differences in speaking behaviour compared to a traditional person independent setup. Such behaviour is very challenging to model as people’s body movements during speech vary greatly. To our knowledge, this is the first time that a transfer learning approach has been considered in the context of speaking status detection using a single body worn accelerometer. We show that by transferring knowledge across subjects, competitive performance scores compared to a person dependent training can be obtained.