Publication detail

Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge

DIEZ SÁNCHEZ, M. BURGET, L. LANDINI, F. WANG, S. ČERNOCKÝ, J.

Original Title

Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge

Type

conference paper

Language

English

Original Abstract

This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input recording. In this paper, we focus on the two x-vector clustering methods employed, namely Agglomerative Hierarchical Clustering followed by a clustering based on Bayesian Hidden Markov Model (BHMM). Even though the system submitted to the challenge had further post-processing steps, we will show that using this BHMM solely is enough to achieve the best performance in the challenge. The analysis will show improvements achieved by optimizing individual processing steps, including a simple procedure to effectively perform "domain adaptation" by Probabilistic Linear Discriminant Analysis model interpolation. All experiments are performed in the DIHARD II evaluation framework.

Keywords

Speaker Diarization, Variational Bayes, HMM, x-vector, DIHARD

Authors

DIEZ SÁNCHEZ, M.; BURGET, L.; LANDINI, F.; WANG, S.; ČERNOCKÝ, J.

Released

4. 5. 2020

Publisher

IEEE Signal Processing Society

Location

Barcelona

ISBN

978-1-5090-6631-5

Book

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Pages from

6519

Pages to

6523

Pages count

5

URL

https://ieeexplore.ieee.org/document/9053982

BibTex

@inproceedings{BUT163963,
  author="Mireia {Diez Sánchez} and Lukáš {Burget} and Federico Nicolás {Landini} and Shuai {Wang} and Jan {Černocký}",
  title="Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge",
  booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
  year="2020",
  pages="6519--6523",
  publisher="IEEE Signal Processing Society",
  address="Barcelona",
  doi="10.1109/ICASSP40776.2020.9053982",
  isbn="978-1-5090-6631-5",
  url="https://ieeexplore.ieee.org/document/9053982"
}