Name
Abstract | ||
|---|---|---|
By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS). In this paper, we investigate the impact of multi-task (MTL) and adversarial learning (ADV) to learn robust stutter features. This is the first-ever preliminary study where MTL and ADV have been employed in stuttering identification (SI). We evaluate our system on the SEP-28k stuttering dataset consisting of ≈ 20 hours of data from 385 podcasts. Our methods show promising results and outperform the baseline in various disfluency classes. We achieve up to 10%, 6.78%, and 2% improvement in repetitions, blocks, and interjections respectively over the baseline. |
Year | Venue | Keywords |
|---|---|---|
2022 | 2022 30th European Signal Processing Conference (EUSIPCO) | stuttering,disfluency,multi-tasking,adversarial,speech disorder |
DocType | ISSN | ISBN |
Conference | 2219-5491 | 978-1-6654-6799-5 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shakeel Ahmad Sheikh | 1 | 0 | 0.34 |
| Md. Sahidullah | 2 | 326 | 24.99 |
| Fabrice Hirsch | 3 | 1 | 0.73 |
| Slim Ouni | 4 | 1 | 0.73 |