Result Details
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
TAWARA, N.
DIEZ SÁNCHEZ, M.
Landini Federico Nicolás, Ph.D., DCGM (FIT)
Silnova Anna, M.Sc., Ph.D., DCGM (FIT)
Ogawa Atsunori, FIT (FIT)
Nakatani Tomohiro, FIT (FIT)
Burget Lukáš, doc. Ing., Ph.D., DCGM (FIT)
ARAKI, S.
Combining end-to-end neural speaker diarization (EEND) with
vector clustering (VC), known as EEND-VC, has gained interest
for leveraging the strengths of both methods. EEND-VC
estimates activities and speaker embeddings for all speakers
within an audio chunk and uses VC to associate these activities
with speaker identities across different chunks. EEND-VC
generates thus multiple streams of embeddings, one for each
speaker in a chunk. We can cluster these embeddings using constrained
agglomerative hierarchical clustering (cAHC), ensuring
embeddings from the same chunk belong to different clusters.
This paper introduces an alternative clustering approach, a
multi-stream extension of the successful Bayesian HMM clustering
of x-vectors (VBx), called MS-VBx. Experiments on
three datasets demonstrate that MS-VBx outperforms cAHC in
diarization and speaker counting performance.
speaker diarization, end-to-end, VBx, clustering
@inproceedings{BUT185573,
author="DELCROIX, M. and TAWARA, N. and DIEZ SÁNCHEZ, M. and LANDINI, F. and SILNOVA, A. and OGAWA, A. and NAKATANI, T. and BURGET, L. and ARAKI, S.",
title="Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization",
booktitle="Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
year="2023",
journal="Proceedings of Interspeech",
volume="2023",
number="08",
pages="3477--3481",
publisher="International Speech Communication Association",
address="Dublin",
doi="10.21437/Interspeech.2023-628",
issn="1990-9772",
url="https://www.isca-speech.org/archive/pdfs/interspeech_2023/delcroix23_interspeech.pdf"
}Speech enhancement front-end for robust automatic speech recognition with large amount of training data, NTT, start: 2023-01-02, end: 2024-01-31, completed