Personalised models for speech detection from body movements using transductive parameter transfer

Ekin Gedik*, Hayley Hung

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)
40 Downloads (Pure)


We investigate the task of detecting speakers in crowded environments using a single body worn triaxial accelerometer. Detection of such behaviour is very challenging to model as people’s body movements during speech vary greatly. Similar to previous studies, by assuming that body movements are indicative of speech, we show experimentally, on a real-world dataset of 3 h including 18 people, that transductive parameter transfer learning (Zen et al. in Proceedings of the 16th international conference on multimodal interaction. ACM, 2014) can better model individual differences in speaking behaviour, significantly improving on the state-of-the-art performance. We also discuss the challenges introduced by the in-the-wild nature of our dataset and experimentally show how they affect detection performance. We strengthen the need for an adaptive approach by comparing the speech detection problem to a more traditional activity (i.e. walking). We provide an analysis of the transfer by considering different source sets which provides a deeper investigation of the nature of both speech and body movements, in the context of transfer learning.

Original languageEnglish
Pages (from-to)723-737
Number of pages15
JournalPersonal and Ubiquitous Computing
Issue number4
Publication statusPublished - 1 Aug 2017


  • Human behaviour
  • Social actions
  • Social signal processing
  • Transfer learning
  • Wearable sensors


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