---
res:
  bibo_abstract:
  - The invention of the Variational Autoencoder enables the application of Neural
    Networks to a wide range of tasks in unsupervised learning, including the field
    of Acoustic Unit Discovery (AUD). The recently proposed Hidden Markov Model Variational
    Autoencoder (HMMVAE) allows a joint training of a neural network based feature
    extractor and a structured prior for the latent space given by a Hidden Markov
    Model. It has been shown that the HMMVAE significantly outperforms pure GMM-HMM
    based systems on the AUD task. However, the HMMVAE cannot autonomously infer the
    number of acoustic units and thus relies on the GMM-HMM system for initialization.
    This paper introduces the Bayesian Hidden Markov Model Variational Autoencoder
    (BHMMVAE) which solves these issues by embedding the HMMVAE in a Bayesian framework
    with a Dirichlet Process Prior for the distribution of the acoustic units, and
    diagonal or full-covariance Gaussians as emission distributions. Experiments on
    TIMIT and Xitsonga show that the BHMMVAE is able to autonomously infer a reasonable
    number of acoustic units, can be initialized without supervision by a GMM-HMM
    system, achieves computationally efficient stochastic variational inference by
    using natural gradient descent, and, additionally, improves the AUD performance
    over the HMMVAE.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Thomas
      foaf_name: Glarner, Thomas
      foaf_surname: Glarner
      foaf_workInfoHomepage: http://www.librecat.org/personId=14169
  - foaf_Person:
      foaf_givenName: Patrick
      foaf_name: Hanebrink, Patrick
      foaf_surname: Hanebrink
  - foaf_Person:
      foaf_givenName: Janek
      foaf_name: Ebbers, Janek
      foaf_surname: Ebbers
      foaf_workInfoHomepage: http://www.librecat.org/personId=34851
  - foaf_Person:
      foaf_givenName: Reinhold
      foaf_name: Haeb-Umbach, Reinhold
      foaf_surname: Haeb-Umbach
      foaf_workInfoHomepage: http://www.librecat.org/personId=242
  dct_date: 2018^xs_gYear
  dct_language: eng
  dct_title: Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic
    Unit Discovery@
...