Detail publikace

A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery

YUSUF, B. ONDEL YANG, L. BURGET, L. ČERNOCKÝ, J. SARAÇLAR, M.

Originální název

A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this work, we propose a hierarchical subspace model for acousticunit discovery. In this approach, we frame the task as one oflearning embeddings on a low-dimensional phonetic subspace, andsimultaneously specify the subspace itself as an embedding on a hyper-subspace. We train the hyper-subspace on a set of transcribedlanguages and transfer it to the target language. In the target language,we infer both the language and unit embeddings in an unsupervisedmanner, and in so doing, we simultaneously learn a subspaceof units specific to that language and the units that dwell on it.We conduct experiments on TIMIT and two low-resource languages:Mboshi and Yoruba. Results show that our model outperforms majoracoustic unit discovery techniques, both in terms of clusteringquality and segmentation accuracy.

Klíčová slova

acoustic unit discovery, hierarchical subspacemodel, unsupervised learning

Autoři

YUSUF, B.; ONDEL YANG, L.; BURGET, L.; ČERNOCKÝ, J.; SARAÇLAR, M.

Vydáno

6. 6. 2021

Nakladatel

IEEE Signal Processing Society

Místo

Toronto, Ontario

ISBN

978-1-7281-7605-5

Kniha

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Strany od

3710

Strany do

3714

Strany počet

5

URL

BibTex

@inproceedings{BUT175792,
  author="YUSUF, B. and ONDEL YANG, L. and BURGET, L. and ČERNOCKÝ, J. and SARAÇLAR, M.",
  title="A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery",
  booktitle="ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
  year="2021",
  pages="3710--3714",
  publisher="IEEE Signal Processing Society",
  address="Toronto, Ontario",
  doi="10.1109/ICASSP39728.2021.9414899",
  isbn="978-1-7281-7605-5",
  url="https://www.fit.vut.cz/research/publication/12523/"
}

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