5 citations · 21 across the 13 of their papers we have counts for
15 papers
ASR ensembling for phoneme intelligibility evaluation of speech anonymizers
Victor Ménestrel, Sebastian Möller, Slim Ouni +1
We present the first phoneme-level intelligibility evaluation of speech anonymizers, assessing the performance of ASR-ensemble-based metrics against measured intelligibility from a…
The Impact of VAE Design on Latent Pose Representations for Diffusion-based Sign Language Production
Guilhem Fauré, Mostafa Sadeghi, Sam Bigeard +1
Latent diffusion approaches to sign language production (SLP) rely on an initial stage that learns an encoding of sign pose sequences, enabling generative modeling in the resulting…
Virtual Speech Therapist: A Clinician-in-the-Loop AI Agent for Speech Therapy Planning
Shakeel Sheikh, Patrick Marmaroli, MD Sahidullah +4
This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized t…
Towards Skeletal and Signer Noise Reduction in Sign Language Production via Quaternion-Based Pose Encoding and Contrastive Learning
Guilhem Fauré, Mostafa Sadeghi, Sam Bigeard +1
One of the main challenges in neural sign language production (SLP) lies in the high intra-class variability of signs, arising from signer morphology and stylistic variety in the t…
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…
Robust Stuttering Detection via Multi-task and Adversarial Learning
Shakeel Ahmad Sheikh, Md Sahidullah, Fabrice Hirsch +1
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 t…
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…
StutterNet: Stuttering Detection Using Time Delay Neural Network
Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch +1
This paper introduces StutterNet, a novel deep learning based stuttering detection capable of detecting and identifying various types of disfluencies. Most of the existing work in…
Speech animation using electromagnetic articulography as motion capture data
Ingmar Steiner, Korin Richmond, Slim Ouni
Electromagnetic articulography (EMA) captures the position and orientation of a number of markers, attached to the articulators, during speech. As such, it performs the same functi…
Using multimodal speech production data to evaluate articulatory animation for audiovisual speech synthesis
Ingmar Steiner, Korin Richmond, Slim Ouni
The importance of modeling speech articulation for high-quality audiovisual (AV) speech synthesis is widely acknowledged. Nevertheless, while state-of-the-art, data-driven approach…
Artimate: an articulatory animation framework for audiovisual speech synthesis
Ingmar Steiner, Slim Ouni
We present a modular framework for articulatory animation synthesis using speech motion capture data obtained with electromagnetic articulography (EMA). Adapting a skeletal animati…
Progress in animation of an EMA-controlled tongue model for acoustic-visual speech synthesis
Ingmar Steiner, Slim Ouni
We present a technique for the animation of a 3D kinematic tongue model, one component of the talking head of an acoustic-visual (AV) speech synthesizer. The skeletal animation app…