---
_id: '11774'
abstract:
- lang: eng
text: In this contribution classification rules for HMM-based speech recognition
in the presence of a mismatch between training and test data are presented. The
observed feature vectors are regarded as corrupted versions of underlying and
unobservable clean feature vectors, which have the same statistics as the training
data. Optimal classification then consists of two steps. First, the posterior
density of the clean feature vector, given the observed feature vectors, has to
be determined, and second, this posterior is employed in a modified classification
rule, which accounts for imperfect estimates. We discuss different variants of
the classification rule and further elaborate on the estimation of the clean speech
feature posterior, using conditional Bayesian estimation. It is shown that this
concept is fairly general and can be applied to different scenarios, such as noisy
or reverberant speech recognition.
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Haeb-Umbach R. Uncertainty Decoding and Conditional Bayesian Estimation. In:
Haeb-Umbach R, Kolossa D, eds. Robust Speech Recognition of Uncertain or Missing
Data. Springer; 2011.'
apa: Haeb-Umbach, R. (2011). Uncertainty Decoding and Conditional Bayesian Estimation.
In R. Haeb-Umbach & D. Kolossa (Eds.), Robust Speech Recognition of Uncertain
or Missing Data. Springer.
bibtex: '@inbook{Haeb-Umbach_2011, title={Uncertainty Decoding and Conditional Bayesian
Estimation}, booktitle={Robust Speech Recognition of Uncertain or Missing Data},
publisher={Springer}, author={Haeb-Umbach, Reinhold}, editor={Haeb-Umbach, Reinhold
and Kolossa, DorotheaEditors}, year={2011} }'
chicago: Haeb-Umbach, Reinhold. “Uncertainty Decoding and Conditional Bayesian Estimation.”
In Robust Speech Recognition of Uncertain or Missing Data, edited by Reinhold
Haeb-Umbach and Dorothea Kolossa. Springer, 2011.
ieee: R. Haeb-Umbach, “Uncertainty Decoding and Conditional Bayesian Estimation,”
in Robust Speech Recognition of Uncertain or Missing Data, R. Haeb-Umbach
and D. Kolossa, Eds. Springer, 2011.
mla: Haeb-Umbach, Reinhold. “Uncertainty Decoding and Conditional Bayesian Estimation.”
Robust Speech Recognition of Uncertain or Missing Data, edited by Reinhold
Haeb-Umbach and Dorothea Kolossa, Springer, 2011.
short: 'R. Haeb-Umbach, in: R. Haeb-Umbach, D. Kolossa (Eds.), Robust Speech Recognition
of Uncertain or Missing Data, Springer, 2011.'
date_created: 2019-07-12T05:28:00Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
editor:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
last_name: Haeb-Umbach
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
language:
- iso: eng
publication: Robust Speech Recognition of Uncertain or Missing Data
publisher: Springer
status: public
title: Uncertainty Decoding and Conditional Bayesian Estimation
type: book_chapter
user_id: '44006'
year: '2011'
...