O-EENC-SD: Efficient Online End-to-End Neural Clustering for Speaker Diarization

TL;DR

O-EENC-SD is a novel online speaker diarization system that offers a hyperparameter-free, efficient, and competitive solution for two-speaker conversations, balancing accuracy and computational complexity.

Contribution

The paper introduces a new online end-to-end neural clustering method with a centroid refinement decoder and a RNN-based stitching mechanism, improving efficiency and simplicity over existing approaches.

Findings

Achieves competitive DER on CallHome dataset

Offers a hyperparameter-free alternative to clustering methods

Provides an efficient solution with low computational cost

Abstract

We introduce O-EENC-SD: an end-to-end online speaker diarization system based on EEND-EDA, featuring a novel RNN-based stitching mechanism for online prediction. In particular, we develop a novel centroid refinement decoder whose usefulness is assessed through a rigorous ablation study. Our system provides key advantages over existing methods: a hyperparameter-free solution compared to unsupervised clustering approaches, and a more efficient alternative to current online end-to-end methods, which are computationally costly. We demonstrate that O-EENC-SD is competitive with the state of the art in the two-speaker conversational telephone speech domain, as tested on the CallHome dataset. Our results show that O-EENC-SD provides a great trade-off between DER and complexity, even when working on independent chunks with no overlap, making the system extremely efficient.