Cross-Corpus Speech Emotion Recognition Based on Deep Domain-Adaptive Convolutional Neural Network

Jiateng LIU  Wenming ZHENG  Yuan ZONG  Cheng LU  Chuangao TANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.2   pp.459-463
Publication Date: 2020/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8136
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
cross-corpus speech emotion recognition,  deep convolutional neural network,  domain adaptation,  

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Summary: 
In this letter, we propose a novel deep domain-adaptive convolutional neural network (DDACNN) model to handle the challenging cross-corpus speech emotion recognition (SER) problem. The framework of the DDACNN model consists of two components: a feature extraction model based on a deep convolutional neural network (DCNN) and a domain-adaptive (DA) layer added in the DCNN utilizing the maximum mean discrepancy (MMD) criterion. We use labeled spectrograms from source speech corpus combined with unlabeled spectrograms from target speech corpus as the input of two classic DCNNs to extract the emotional features of speech, and train the model with a special mixed loss combined with a cross-entrophy loss and an MMD loss. Compared to other classic cross-corpus SER methods, the major advantage of the DDACNN model is that it can extract robust speech features which are time-frequency related by spectrograms and narrow the discrepancies between feature distribution of source corpus and target corpus to get better cross-corpus performance. Through several cross-corpus SER experiments, our DDACNN achieved the state-of-the-art performance on three public emotion speech corpora and is proved to handle the cross-corpus SER problem efficiently.