Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bayesian Network View on Acoustic Model-Based Techniques for Robust
  Speech Recognition   This article provides a unifying Bayesian network 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
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches.

35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc