  In this paper, we propose to utilise diffusion models for data augmentation
in speech emotion recognition (SER). In particular, we present an effective
approach to utilise improved denoising diffusion probabilistic models (IDDPM)
to generate synthetic emotional data. We condition the IDDPM with the textual
embedding from bidirectional encoder representations from transformers (BERT)
to generate high-quality synthetic emotional samples in different speakers'
voices\footnote{synthetic samples URL:
\url{https://emulationai.com/research/diffusion-ser.}}. We implement a series
of experiments and show that better quality synthetic data helps improve SER
performance. We compare results with generative adversarial networks (GANs) and
show that the proposed model generates better-quality synthetic samples that
can considerably improve the performance of SER when augmented with synthetic
data.
