Adapting the NICT-JLE Corpus for Disfluency Detection Models
This addresses the problem of restricted access to datasets for researchers working on disfluency detection in learner speech, enabling easier comparison and development of improved models, though it is incremental as it adapts an existing corpus rather than creating a new method.
The paper tackled the lack of standardized datasets for disfluency detection in learner speech by adapting the NICT-JLE corpus, containing about 300 hours of English learners' oral proficiency tests, into a format suitable for model training and evaluation, resulting in a standardized train, heldout, and test set for future research.
The detection of disfluencies such as hesitations, repetitions and false starts commonly found in speech is a widely studied area of research. With a standardised process for evaluation using the Switchboard Corpus, model performance can be easily compared across approaches. This is not the case for disfluency detection research on learner speech, however, where such datasets have restricted access policies, making comparison and subsequent development of improved models more challenging. To address this issue, this paper describes the adaptation of the NICT-JLE corpus, containing approximately 300 hours of English learners' oral proficiency tests, to a format that is suitable for disfluency detection model training and evaluation. Points of difference between the NICT-JLE and Switchboard corpora are explored, followed by a detailed overview of adaptations to the tag set and meta-features of the NICT-JLE corpus. The result of this work provides a standardised train, heldout and test set for use in future research on disfluency detection for learner speech.