Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to detect dysarthria from raw speech   Speech classifiers of paralinguistic traits traditionally learn from diverse
hand-crafted low-level features, by selecting the relevant information for the
task at hand. We explore an alternative to this selection, by learning jointly
the classifier, and the feature extraction. Recent work on speech recognition
has shown improved performance over speech features by learning from the
waveform. We extend this approach to paralinguistic classification and propose
a neural network that can learn a filterbank, a normalization factor and a
compression power from the raw speech, jointly with the rest of the
architecture. We apply this model to dysarthria detection from sentence-level
audio recordings. Starting from a strong attention-based baseline on which
mel-filterbanks outperform standard low-level descriptors, we show that
learning the filters or the normalization and compression improves over fixed
features by 10% absolute accuracy. We also observe a gain over OpenSmile
features by learning jointly the feature extraction, the normalization, and the
compression factor with the architecture. This constitutes a first attempt at
learning jointly all these operations from raw audio for a speech
classification task.

21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin