Publication Details
Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery
Vydana Hari Krishna
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Bayesian Inference, Hidden Markov Model, Subspace Model, Variational Bayes,
Low-resource languages, Acoustic Unit Discovery
This work tackles the problem of learning a set of language specific acoustic
units from unlabeled speech recordings given a set of labeled recordings from
other languages. Our approach may be described by the following two steps
procedure: first the model learns the notion of acoustic units from the labelled
data and then the model uses its knowledge to find new acoustic units on the
target language. We implement this process with the Bayesian Subspace Hidden
Markov Model (SHMM), a model akin to the Subspace Gaussian Mixture Model (SGMM)
where each low dimensional embedding represents an acoustic unit rather than just
a HMMs state. The subspace is trained on 3 languages from the GlobalPhone corpus
(German, Polish and Spanish) and the AUs are discovered on the TIMIT corpus.
Results, measured in equivalent Phone Error Rate, show that this approach
significantly outperforms previous HMM based acoustic units discovery systems and
compares favorably with the Variational Auto Encoder-HMM.
@inproceedings{BUT159991,
author="Lucas Antoine Francois {Ondel} and Hari Krishna {Vydana} and Lukáš {Burget} and Jan {Černocký}",
title="Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery",
booktitle="Proceedings of Interspeech 2019",
year="2019",
journal="Proceedings of Interspeech",
volume="2019",
number="9",
pages="261--265",
publisher="International Speech Communication Association",
address="Graz",
doi="10.21437/Interspeech.2019-2224",
issn="1990-9772",
url="https://www.isca-speech.org/archive/Interspeech_2019/pdfs/2224.pdf"
}