Dynamic Time-alignment Of Dimensional Annotations Of Emotion Using Recurrent Neural Networks

Abstract

Most automatic emotion recognition systems exploit time-continuous annotations of emotion to provide fine-grained descriptions of spontaneous expressions as observed in real-life interactions. As emotion is rather subjective, its annotation is usually performed by several annotators who provide a trace for a given dimension, i.e. a time-continuous series describing a dimension such as arousal or valence. However, annotations of the same expression are rarely consistent between annotators, either in time or in value, which adds bias and delay in the trace that is used to learn predictive models of emotion. We therefore propose a method that can dynamically compensate inconsistencies across annotations and synchronise the traces with the corresponding acoustic features using Recurrent Neural Networks. Experimental evaluations were carried on several emotion data sets that include Chinese, French, German, and Hungarian participants who interacted remotely in either noise-free conditions o

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyalisamir2022dynamic

Related papers

• Jointly Aligning And Predicting Continuous Emotion Annotations (2019)8.60
• Capturing Long-term Temporal Dependencies With Convolutional Networks For Continuous Emotion Recognition (2017)10.48
• A Transfer Learning Method For Speech Emotion Recognition From Automatic Speech Recognition (2020)0.00
• Audio Visual Emotion Recognition With Temporal Alignment And Perception Attention (2016)0.00
• Emotion Recognition By Fusing Time Synchronous And Time Asynchronous Representations (2020)13.05
• The Whole Is Bigger Than The Sum Of Its Parts: Modeling Individual Annotators To Capture Emotional Variability (2024)3.58
• Unifying The Discrete And Continuous Emotion Labels For Speech Emotion Recognition (2022)0.00
• Automatically Augmenting An Emotion Dataset Improves Classification Using Audio (2018)0.00