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 Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1526–1532
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.190
- DOI:
- Cite (ACL):
- Jane Wottawa, Marie Tahon, Apolline Marin, and Nicolas Audibert. 2020. Towards Interactive Annotation for Hesitation in Conversational Speech. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1526–1532, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Towards Interactive Annotation for Hesitation in Conversational Speech (Wottawa et al., LREC 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.lrec-1.190.pdf