2 research outputs found
Learning Repeatable Speech Embeddings Using An Intra-class Correlation Regularizer
A good supervised embedding for a specific machine learning task is only
sensitive to changes in the label of interest and is invariant to other
confounding factors. We leverage the concept of repeatability from measurement
theory to describe this property and propose to use the intra-class correlation
coefficient (ICC) to evaluate the repeatability of embeddings. We then propose
a novel regularizer, the ICC regularizer, as a complementary component for
contrastive losses to guide deep neural networks to produce embeddings with
higher repeatability. We use simulated data to explain why the ICC regularizer
works better on minimizing the intra-class variance than the contrastive loss
alone. We implement the ICC regularizer and apply it to three speech tasks:
speaker verification, voice style conversion, and a clinical application for
detecting dysphonic voice. The experimental results demonstrate that adding an
ICC regularizer can improve the repeatability of learned embeddings compared to
only using the contrastive loss; further, these embeddings lead to improved
performance in these downstream tasks.Comment: Accepted by NeurIPS 202