Towards Interactive Annotation for Hesitation in Conversational Speech

Jane Wottawa, Marie Tahon, Apolline Marin, Nicolas Audibert


Abstract
Manual annotation of speech corpora is expensive in both human resources and time. Furthermore, recognizing affects in spontaneous, non acted speech presents a challenge for humans and machines. The aim of the present study is to automatize the labeling of hesitant speech as a marker of expressed uncertainty. That is why, the NCCFr-corpus was manually annotated for ‘degree of hesitation’ on a continuous scale between -3 and 3 and the affective dimensions ‘activation, valence and control’. In total, 5834 chunks of the NCCFr-corpus were manually annotated. Acoustic analyses were carried out based on these annotations. Furthermore, regression models were trained in order to allow automatic prediction of hesitation for speech chunks that do not have a manual annotation. Preliminary results show that the number of filled pauses as well as vowel duration increase with the degree of hesitation, and that automatic prediction of the hesitation degree reaches encouraging RMSE results of 1.6.
Anthology ID:
2020.lrec-1.190
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1526–1532
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.190
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.190.pdf