mlsanityData to 7 Oct 2026

Look who's not talking

arXiv 2011.14885 ↗First posted 30 Nov 2020cs.SD eess.AScited by 6

Not on the map. Why?

Abstract (arXiv)

The objective of this work is speaker diarisation of speech recordings 'in the wild'. The ability to determine speech segments is a crucial part of diarisation systems, accounting for a large proportion of errors. In this paper, we present a simple but effective solution for speech activity detection based on the speaker embeddings. In particular, we discover that the norm of the speaker embedding is an extremely effective indicator of speech activity. The method does not require an independent model for speech activity detection, therefore allows speaker diarisation to be performed using a unified representation for both speaker modelling and speech activity detection. We perform a number of experiments on in-house and public datasets, in which our method outperforms popular baselines.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 2; 2025: 1.

6 papers.