9 papers
Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba
Yannick Yomie Nzeuhang, Paulin Melatagia Yonta, Marie Tahon
Acoustic representation learning is crucial for speech processing, yet low-resource languages (LRLs) face severe data scarcity, limiting the effectiveness of traditional and self-s…
Low resource cross-modal alignment using HGNN to enhance speech representation
Yannick Yomie Nzeuhang, Marie Tahon, Paulin Melatagia Yonta
Speech-text space alignment is a multimodal representation learning method consisting to map different speech and text into a shared representation space, leading to enrichment of…
Sparse deepfake detection promotes better disentanglement
Antoine Teissier, Marie Tahon, Nicolas Dugué +1
Due to the rapid progress of speech synthesis, deepfake detection has become a major concern in the speech processing community. Because it is a critical task, systems must not onl…
Sparse Autoencoders Make Audio Foundation Models more Explainable
Théo Mariotte, Martin Lebourdais, Antonio Almudévar +3
Audio pretrained models are widely employed to solve various tasks in speech processing, sound event detection, or music information retrieval. However, the representations learned…
Automatic Voice Identification after Speech Resynthesis using PPG
Thibault Gaudier, Marie Tahon, Anthony Larcher +1
Speech resynthesis is a generic task for which we want to synthesize audio with another audio as input, which finds applications for media monitors and journalists.Among different…
Predefined Prototypes for Intra-Class Separation and Disentanglement
Antonio Almudévar, Théo Mariotte, Alfonso Ortega +4
Prototypical Learning is based on the idea that there is a point (which we call prototype) around which the embeddings of a class are clustered. It has shown promising results in s…
Explainable by-design Audio Segmentation through Non-Negative Matrix Factorization and Probing
Martin Lebourdais, Théo Mariotte, Antonio Almudévar +2
Audio segmentation is a key task for many speech technologies, most of which are based on neural networks, usually considered as black boxes, with high-level performances. However,…
Unsupervised Multiple Domain Translation through Controlled Disentanglement in Variational Autoencoder
Antonio Almudévar, Théo Mariotte, Alfonso Ortega +1
Unsupervised Multiple Domain Translation is the task of transforming data from one domain to other domains without having paired data to train the systems. Typically, methods based…
An Explainable Proxy Model for Multiabel Audio Segmentation
Théo Mariotte, Antonio Almudévar, Marie Tahon +1
Audio signal segmentation is a key task for automatic audio indexing. It consists of detecting the boundaries of class-homogeneous segments in the signal. In many applications, exp…