Learning Disentangled Speech Representations
Yusuf Brima Ulf Krumnack Simone Pika Gunther Heidemann
Disentangled representation learning from speech remains limited despite its
importance in many application domains. A key challenge is the lack of speech
datasets with known generative factors to evaluate methods. This paper proposes
SynSpeech: a novel synthetic speech dataset with ground truth factors enabling
research on disentangling speech representations. We plan to present a
comprehensive study evaluating supervised techniques using established
supervised disentanglement metrics. This benchmark dataset and framework
address the gap in the rigorous evaluation of state-of-the-art disentangled
speech representation learning methods. Our findings will provide insights to
advance this underexplored area and enable more robust speech representations.