Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-supervised Learning with Sparse Autoencoders in Phone
  Classification   We propose the application of a semi-supervised learning method to improve
the performance of acoustic modelling for automatic speech recognition based on
deep neural net- works. As opposed to unsupervised initialisation followed by
supervised fine tuning, our method takes advantage of both unlabelled and
labelled data simultaneously through mini- batch stochastic gradient descent.
We tested the method with varying proportions of labelled vs unlabelled
observations in frame-based phoneme classification on the TIMIT database. Our
experiments show that the method outperforms standard supervised training for
an equal amount of labelled data and provides competitive error rates compared
to state-of-the-art graph-based semi-supervised learning techniques.

11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin