A Bayesian view on acoustic model-based techniques for robust speech recognition

Journal article
(Review article)


Publication Details

Author(s): Maas R, Hümmer C, Sehr A, Kellermann W
Journal: Eurasip Journal on Advances in Signal Processing
Publication year: 2015
Volume: 2015
Journal issue: 1
Pages range: 1-16
ISSN: 1687-6180


Abstract


This article provides a unifying Bayesian view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition. The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition. These extensions, in turn, can in many cases be motivated from an underlying observation model that relates clean and distorted feature vectors. By identifying and converting the observation models into a Bayesian network representation, we formulate the corresponding compensation rules. We thus summarize the various approaches as approximations or modifications of the same Bayesian decoding rule leading to a unified view on known derivations as well as to new formulations for certain approaches.



FAU Authors / FAU Editors

Hümmer, Christian
Professur für Nachrichtentechnik
Kellermann, Walter Prof. Dr.-Ing.
Professur für Nachrichtentechnik
Maas, Roland Dr.-Ing.
Lehrstuhl für Multimediakommunikation und Signalverarbeitung


External institutions
Ostbayerische Technische Hochschule Regensburg (OTH)


How to cite

APA:
Maas, R., Hümmer, C., Sehr, A., & Kellermann, W. (2015). A Bayesian view on acoustic model-based techniques for robust speech recognition. Eurasip Journal on Advances in Signal Processing, 2015(1), 1-16. https://dx.doi.org/10.1186/s13634-015-0287-x

MLA:
Maas, Roland, et al. "A Bayesian view on acoustic model-based techniques for robust speech recognition." Eurasip Journal on Advances in Signal Processing 2015.1 (2015): 1-16.

BibTeX: 

Last updated on 2018-07-08 at 22:27