Publication Details
A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery
Ondel Yang Lucas Antoine Francois, Mgr., Ph.D. (DCGM FIT BUT)
Burget Lukáš, doc. Ing., Ph.D. (DCGM FIT BUT)
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT)
Saraçlar Murat (UBOGAZ)
acoustic unit discovery, hierarchical subspace model, unsupervised learning
In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic subspace, and simultaneously specify the subspace itself as an embedding on a hyper- subspace. We train the hyper-subspace on a set of transcribed languages and transfer it to the target language. In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of 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 major acoustic unit discovery techniques, both in terms of clustering quality and segmentation accuracy.
@INPROCEEDINGS{FITPUB12523,
author = "Bolaji Yusuf and Francois Antoine Lucas Yang Ondel and Luk\'{a}\v{s} Burget and Jan \v{C}ernock\'{y} and Murat Sara\c{c}lar",
title = "A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery",
pages = "3710--3714",
booktitle = "ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
year = 2021,
location = "Toronto, Ontario, CA",
publisher = "IEEE Signal Processing Society",
ISBN = "978-1-7281-7605-5",
doi = "10.1109/ICASSP39728.2021.9414899",
language = "english",
url = "https://www.fit.vut.cz/research/publication/12523"
}