5 citations · 21 across the 12 of their papers we have counts for
5 papers · 1 filter
Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings
Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch +1
The adoption of advanced deep learning architectures in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work int…
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning
Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch +1
Stuttering is a neuro-developmental speech impairment characterized by uncontrolled utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is c…
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch +1
In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifical…
Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch +1
The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work…
Machine Learning for Stuttering Identification: Review, Challenges and Future Directions
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch +1
Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds. Stuttering identification is an interesting interdisc…