Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ASR Performance Prediction on Unseen Broadcast Programs using
  Convolutional Neural Networks   In this paper, we address a relatively new task: prediction of ASR
performance on unseen broadcast programs. We first propose an heterogenous
French corpus dedicated to this task. Two prediction approaches are compared: a
state-of-the-art performance prediction based on regression (engineered
features) and a new strategy based on convolutional neural networks (learnt
features). We particularly focus on the combination of both textual (ASR
transcription) and signal inputs. While the joint use of textual and signal
features did not work for the regression baseline, the combination of inputs
for CNNs leads to the best WER prediction performance. We also show that our
CNN prediction remarkably predicts the WER distribution on a collection of
speech recordings.

15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ