UCL Discovery Stage
UCL home » Library Services » Electronic resources » UCL Discovery Stage

Within-Speaker Features for Native Language Recognition in the Interspeech 2016 Computational Paralinguistics Challenge

Huckvale, MA; (2016) Within-Speaker Features for Native Language Recognition in the Interspeech 2016 Computational Paralinguistics Challenge. In: Morgan, N, (ed.) [Interspeech 2016: Proceedings]. (pp. pp. 2403-2407). International Speech Communication Association Green open access

[thumbnail of is2016compar-final.pdf]
Preview
Text
is2016compar-final.pdf - Accepted Version

Download (169kB) | Preview

Abstract

The Interspeech 2016 Native Language recognition challenge was to identify the first language of 867 speakers from their spoken English. Effectively this was an L2 accent recognition task where the L1 was one of eleven languages. The lack of transcripts of the spontaneous speech recordings meant that the currently best performing accent recognition approach (ACCDIST) developed by the author could not be applied. Instead, the objectives of this study were to explore whether within-speaker features found to be effective in ACCDIST would also have value within a contemporary GMM-based accent recognition approach. We show that while Gaussian mean supervectors provide the best performance on this task, small gains may be had by fusing the mean supervector system with a system based on within-speaker Gaussian mixture distances.

Type: Proceedings paper
Title: Within-Speaker Features for Native Language Recognition in the Interspeech 2016 Computational Paralinguistics Challenge
Event: Interspeech 2016: 17th Annual Conference of the International Speech Communication Association, 8-12 September 2016, San Francisco, California, USA
Location: San Francisco, USA
Dates: 08 September 2016 - 12 September 2016
Open access status: An open access version is available from UCL Discovery
DOI: 10.21437/Interspeech.2016-1466
Publisher version: http://www.isca-speech.org/archive/Interspeech_201...
Language: English
Additional information: Paper ID code: 1466. Copyright © 2016 International Speech Communication Association.
Keywords: accent recognition, second language, computational paralinguistics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Speech, Hearing and Phonetic Sciences
URI: https://discovery-pp.ucl.ac.uk/id/eprint/1524227
Downloads since deposit
12,388Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item