2022
DOI: 10.48550/arxiv.2204.01735
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Robust Stuttering Detection via Multi-task and Adversarial Learning

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 p… Show more

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