Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DNN-based uncertainty estimation for weighted DNN-HMM ASR   In this paper, the uncertainty is defined as the mean square error between a
given enhanced noisy observation vector and the corresponding clean one. Then,
a DNN is trained by using enhanced noisy observation vectors as input and the
uncertainty as output with a training database. In testing, the DNN receives an
enhanced noisy observation vector and delivers the estimated uncertainty. This
uncertainty in employed in combination with a weighted DNN-HMM based speech
recognition system and compared with an existing estimation of the noise
cancelling uncertainty variance based on an additive noise model. Experiments
were carried out with Aurora-4 task. Results with clean, multi-noise and
multi-condition training are presented.

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The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat