Work

Semi-supervised Ladder Networks for Speech Emotion Recognition

Year: 2019

Type: article

Abstract: As a major component of speech signal processing, speech emotion recognition has become increasingly essential to understanding human communication. Benefitting from deep learning, many researchers have proposed various unsupervised models to extract effective emotional features and supervised models to train emotion recognition systems. In this paper, we utilize semi-supervised ladder networks for speech emotion recognition. The model is trained by minimizing the supervised loss and auxiliary unsupervised cost function. The addition of the unsupervised auxiliary task provides powerful discriminative representations of the input features, and is also regarded as the regularization of the emotional supervised task. We also compare the ladder network with other classical autoencoder structures. The experiments were conducted on the interactive emotional dyadic motion capture (IEMOCAP) database, and the results reveal that the proposed methods achieve superior performance with a small number of labelled data and achieves better performance than other methods.

Source: Machine Intelligence Research

DOI: 10.1007/s11633-019-1175-x

Authors: Jian-Hua Tao, Jian Huang, Ya Li, Zheng Lian, Ming-Yue Niu

Institutions: Chinese Academy of Sciences, Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, University of Chinese Academy of Sciences

Language: English

Topic: Emotion and Mood Recognition

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,690

Funder: National Natural Science Foundation of China