most citedMultimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

43 citations · 47 across the 4 of their papers we have counts for

collaborators

5 papers

eess.AS20191 cited

Bag-of-Audio-Words based on Autoencoder Codebook for Continuous Emotion Prediction

Mohammed Senoussaoui, Patrick Cardinal, Alessandro Lameiras Koerich

In this paper we present a novel approach for extracting a Bag-of-Words (BoW) representation based on a Neural Network codebook. The conventional BoW model is based on a dictionary…

cs.CV201943 cited

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

Juan D. S. Ortega, Mohammed Senoussaoui, Eric Granger +3

This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independe…

cs.LG2019

Emotion Recognition Using Fusion of Audio and Video Features

Juan D. S. Ortega, Patrick Cardinal, Alessandro L. Koerich

In this paper we propose a fusion approach to continuous emotion recognition that combines visual and auditory modalities in their representation spaces to predict the arousal and…

cs.SD2019

Speaker Sincerity Detection based on Covariance Feature Vectors and Ensemble Methods

Mohammed Senoussaoui, Patrick Cardinal, Najim Dehak +1

Automatic measuring of speaker sincerity degree is a novel research problem in computational paralinguistics. This paper proposes covariance-based feature vectors to model speech a…

cs.SD20193 cited

End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network

Sajjad Abdoli, Patrick Cardinal, Alessandro Lameiras Koerich

In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from th…