  In recent years, speech emotion recognition technology is of great
significance in industrial applications such as call centers, social robots and
health care. The combination of speech recognition and speech emotion
recognition can improve the feedback efficiency and the quality of service.
Thus, the speech emotion recognition has been attracted much attention in both
industry and academic. Since emotions existing in an entire utterance may have
varied probabilities, speech emotion is likely to be ambiguous, which poses
great challenges to recognition tasks. However, previous studies commonly
assigned a single-label or multi-label to each utterance in certain. Therefore,
their algorithms result in low accuracies because of the inappropriate
representation. Inspired by the optimally interacting theory, we address the
ambiguous speech emotions by proposing a novel multi-classifier interactive
learning (MCIL) method. In MCIL, multiple different classifiers first mimic
several individuals, who have inconsistent cognitions of ambiguous emotions,
and construct new ambiguous labels (the emotion probability distribution).
Then, they are retrained with the new labels to interact with their cognitions.
This procedure enables each classifier to learn better representations of
ambiguous data from others, and further improves the recognition ability. The
experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate
that MCIL does not only improve each classifier's performance, but also raises
their recognition consistency from moderate to substantial.
