Search Results for author:
Found 14 papers, 3 papers with code
Efficiency-oriented approaches for self-supervised speech representation learning
Self-supervised learning enables the training of large neural models without the need for large, labeled datasets.
Is one brick enough to break the wall of spoken dialogue state tracking?
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs (a. k. a dialogue state tracking) is key to a smooth interaction.
Are cascade dialogue state tracking models speaking out of turn in spoken dialogues?
In Task-Oriented Dialogue (TOD) systems, correctly updating the system's understanding of the user's needs is key to a smooth interaction.
OLISIA: a Cascade System for Spoken Dialogue State Tracking
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language. In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Are E2E ASR models ready for an industrial usage?
The Automated Speech Recognition (ASR) community experiences a major turning point with the rise of the fully-neural (End-to-End, E2E) approaches.
Efficient conformer: Progressive downsampling and grouped attention for automatic speech recognition
The recently proposed Conformer architecture has shown state-of-the-art performances in Automatic Speech Recognition by combining convolution with attention to model both local and global dependencies.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards a General Model of Knowledge for Facial Analysis by Multi-Source Transfer Learning
This model outperforms its teacher on novel tasks, achieving results on par with state-of-the-art methods on 15 facial analysis tasks (and domains), at an affordable training cost.
MFAS: Multimodal Fusion Architecture Search
We tackle the problem of finding good architectures for multimodal classification problems.
Multi-Level Sensor Fusion with Deep Learning
In the context of deep learning, this article presents an original deep network, namely CentralNet, for the fusion of information coming from different sensors.
The Many Moods of Emotion
This paper presents a novel approach to the facial expression generation problem.
CentralNet: a Multilayer Approach for Multimodal Fusion
This paper proposes a novel multimodal fusion approach, aiming to produce best possible decisions by integrating information coming from multiple media.
An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets
This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition.
CAKE: Compact and Accurate K-dimensional representation of Emotion
Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks. Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e. g. anger, happiness, sadness, etc.)
Ranked #22 on
Facial Expression Recognition (FER)
on AffectNet
(Accuracy (7 emotion) metric)
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
This paper addresses the question of emotion classification.
