Detail publikace

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.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

4. 5. 2020

Nakladatel

IEEE Signal Processing Society

Místo

Barcelona

ISBN

978-1-5090-6631-5

Kniha

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

Strany od

6519

Strany do

6523

Strany počet

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"
}

Dokumenty

diez_icassp2020_09053982.pdf