  Despite the widespread utilization of deep neural networks (DNNs) for speech
emotion recognition (SER), they are severely restricted due to the paucity of
labeled data for training. Recently, segment-based approaches for SER have been
evolving, which train backbone networks on shorter segments instead of whole
utterances, and thus naturally augments training examples without additional
resources. However, one core challenge remains for segment-based approaches:
most emotional corpora do not provide ground-truth labels at the segment level.
To supervisely train a segment-based emotion model on such datasets, the most
common way assigns each segment the corresponding utterance's emotion label.
However, this practice typically introduces noisy (incorrect) labels as
emotional information is not uniformly distributed across the whole utterance.
On the other hand, DNNs have been shown to easily over-fit a dataset when being
trained with noisy labels. To this end, this work proposes a simple and
effective deep self-learning (DSL) framework, which comprises a procedure to
progressively correct segment-level labels in an iterative learning manner. The
DSL method produces dynamically-generated and soft emotion labels, leading to
significant performance improvements. Experiments on three well-known emotional
corpora demonstrate noticeable gains using the proposed method.
