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Found 11 papers, 0 papers with code
Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings
The adoption of advanced deep learning architectures in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets.
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning
In addition, we propose a multi-contextual (MC) StutterNet, which exploits different contexts of the stuttered speech, resulting in an overall improvement of 4. 48% in F 1 over the single context based MB StutterNet.
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge
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, specifically the stuttering sub-challenge.
Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection
The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets.
Robust Stuttering Detection via Multi-task and Adversarial Learning
By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS).
Machine Learning for Stuttering Identification: Review, Challenges and Future Directions
Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds.
StutterNet: Stuttering Detection Using Time Delay Neural Network
Compared to the existing work, which depends on the ASR module, our method relies solely on the acoustic signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Disfluences normales vs. Disfluences s\'ev\`eres : une \'etude acoustique (Normal disfluences vs)
L{'}objectif de cette recherche est d{'}{\'e}tudier les caract{\'e}ristiques acoustiques et perceptives des disfluences normales et s{\'e}v{\`e}res.
