Abstract
The ACM Multimedia 2022 Computational Paralinguistics Challenge (ComParE) featured a sub-challenge on the classification of stuttering in order to bring attention to this important topic and engage a wider research community. Stuttering is a complex speech disorder characterized by blocks, prolongations of sounds and syllables, and repetitions of sounds and words. Accurately classifying the symptoms of stuttering has implications for the development of self-help tools and specialized automatic speech recognition systems (ASR) that can handle atypical speech patterns. This paper provides a review of the challenge contributions and improves upon them with new state-of-the-art classification results for the KSF-C dataset, and explores cross-language training to demonstrate the potential of datasets in multiple languages. To facilitate further research and reproducibility, the full KSF-C dataset, including test-set labels, is also released.
Original language | English |
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Article number | 101519 |
Journal | Computer Speech and Language |
Volume | 81 |
DOIs | |
State | Published - Jun 2023 |
Externally published | Yes |
Keywords
- ComParE challenge
- Dysfluency
- Paralinguistics
- Pathological speech
- Stuttering