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.

本文研究了如何使用卷积神经网络对未见过的广播节目语音识别性能进行预测，并比较了传统基于回归的性能预测方法和基于卷积神经网络的新策略。该研究尤其关注了音频信号和 ASR 文本样本的联合使用对预测准确性的影响，并证实了基于卷积神经网络的预测能够非常准确地预测广播节目中的语音识别错误率分布。