2018
INTERSPEECH
INTERSPEECH 2018
An End-to-End Deep Learning Framework for Speech Emotion Recognition of Atypical Individuals
Abstract
The goal of the ongoing ComParE 2018 Atypical Affect sub-challenge is to recognize the emotional states of atypical individuals. In this work, we present three modeling methods under the end-to-end learning framework, namely CNN combined with extended features, CNN+RNN and ResNet, respectively. Furthermore, we investigate multiple data augmentation, balancing and sampling methods to further enhance the system performance. The experimental results show that data balancing and augmentation increase the unweighted accuracy (UAR) by 10% absolutely. After score level fusion, our proposed system achieves 48.8% UAR on the develop dataset.
🌉
Interdisciplinary Bridge
— Deep Learning and Machine Learning
🐣
Hot Topic Early Bird
— data augmentation
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🧭
Keyword Pioneer
— atypical individual
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Application Areas > Data Augmentation
Deep Learning > Architectures > Neural Networks
Speech & Audio > Analysis > Speech Analysis
Healthcare & Medicine > Clinical > Medical AI
Machine Learning > Learning Types > Deep Learning
Deep Learning > Learning Types > Deep Learning