Speaking Status Detection from Body Movements Using Transductive Parameter Transfer

Conference Paper (2016)
Authors

Ekin Gedik (TU Delft - Pattern Recognition and Bioinformatics)

HS Hung (TU Delft - Pattern Recognition and Bioinformatics)

Research Group
Pattern Recognition and Bioinformatics
To reference this document use:
https://doi.org/10.1145/2968219.2971444
More Info
expand_more
Publication Year
2016
Language
English
Research Group
Pattern Recognition and Bioinformatics
Pages (from-to)
69-72
ISBN (electronic)
978-1-4503-4462-3
DOI:
https://doi.org/10.1145/2968219.2971444

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.


No files available

Metadata only record. There are no files for this record.