ISCA Archive Interspeech 2023
ISCA Archive Interspeech 2023

A Preliminary Study on Augmenting Speech Emotion Recognition using a Diffusion Model

Mohammad Ibrahim Malik, Siddique Latif, Raja Jurdak, Björn W. Schuller

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' voiceswe uploaded the synthetic samples for reviewers to listen.. 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.