Identification of Primary and Collateral Tracks in Stuttered Speech

Rachid Riad, Anne-Catherine Bachoud-Lévi, Frank Rudzicz, Emmanuel Dupoux


Abstract
Disfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective aiming at modeling these events and detect them for downstream tasks. In addition, previous works often used different metrics depending on whether the input features are text or speech, making it difficult to compare the different contributions. Here, we introduce a new evaluation framework for disfluency detection inspired by the clinical and NLP perspective together with the theory of performance from (Clark, 1996) which distinguishes between primary and collateral tracks. We introduce a novel forced-aligned disfluency dataset from a corpus of semi-directed interviews, and present baseline results directly comparing the performance of text-based features (word and span information) and speech-based (acoustic-prosodic information). Finally, we introduce new audio features inspired by the word-based span features. We show experimentally that using these features outperformed the baselines for speech-based predictions on the present dataset.
Anthology ID:
2020.lrec-1.208
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1681–1688
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.208
DOI:
Bibkey:
Cite (ACL):
Rachid Riad, Anne-Catherine Bachoud-Lévi, Frank Rudzicz, and Emmanuel Dupoux. 2020. Identification of Primary and Collateral Tracks in Stuttered Speech. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1681–1688, Marseille, France. European Language Resources Association.
Cite (Informal):
Identification of Primary and Collateral Tracks in Stuttered Speech (Riad et al., LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.208.pdf