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 (approx) 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.
@article{arxiv.2204.01735,
title = {Robust Stuttering Detection via Multi-task and Adversarial Learning},
author = {Shakeel Ahmad Sheikh and Md Sahidullah and Fabrice Hirsch and Slim Ouni},
journal= {arXiv preprint arXiv:2204.01735},
year = {2022}
}
Comments
Under Review in European Signal Processing Conference 2022