BUT System Description for DIHARD Speech Diarization Challenge 2019
Not on the map. Why?
Abstract (arXiv)
This paper describes the systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge. For tracks 1 and 2 the systems were based on performing agglomerative hierarchical clustering (AHC) over x-vectors, followed by the Bayesian Hidden Markov Model (HMM) with eigenvoice priors applied at x-vector level followed by the same approach applied at frame level. For tracks 3 and 4, the systems were based on performing AHC using x-vectors extracted on all channels.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Oct 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 4; 2022: 1.
5 papers.
- 2021The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lapcited by 8
- 2021Multi-scale speaker embedding-based graph attention networks for speaker diarisationcited by 7
- 2021Adapting Speaker Embeddings for Speaker Diarisationcited by 4
- 2022High-resolution embedding extractor for speaker diarisationcited by 1
- 2021Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activitycited by 0
- 2022High-resolution embedding extractor for speaker diarisation8 Nov 2022
- 2021Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity7 Oct 2021
- 2021Multi-scale speaker embedding-based graph attention networks for speaker diarisation7 Oct 2021
- 2021Adapting Speaker Embeddings for Speaker Diarisation7 Apr 2021
- 2021The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap2 Feb 2021