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.

翻译：本文提出利用扩散模型进行语音情感识别（SER）中的数据增强。具体而言，我们提出一种有效方法，采用改进的降噪扩散概率模型（IDDPM）生成合成情感数据。通过将双向编码器表示（BERT）的文本嵌入作为条件输入IDDPM，生成不同说话者声音的高质量合成情感样本（合成样本链接：\url{https://emulationai.com/research/diffusion-ser.））。我们开展系列实验表明，更高质量的合成数据有助于提升SER性能。与生成对抗网络（GANs）的结果对比显示，所提模型生成的合成样本质量更高，当与合成数据联合增强时，能显著改善SER的性能。