Performance prediction of automatic speech recognition systems using convolutional neural networks

被引：0

作者：

Elloumi, Zied ^{[1
,2
]}

Lecouteux, Benjamin ^{[2
]}

Galibert, Olivier ^{[1
]}

Besacier, Laurent ^{[2
]}

机构：

[1] Lab Natl Metrol & Essais LNE, Paris, France

[2] Univ Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France

来源：

TRAITEMENT AUTOMATIQUE DES LANGUES | 2018年 / 59卷 / 02期

关键词：

performance prediction; large vocabulary continuous speech recognition; convolutional neural networks;

D O I：

暂无

中图分类号：

H0 [语言学];

学科分类号：

030303 ; 0501 ; 050102 ;

摘要：

This paper focuses on the ASR performance prediction task. Two prediction approaches are compared: a state-of-the-art performance prediction based on engineered features and a new strategy based on learnt features using convolutional neural networks. We also try to better understand which information is captured by the deep model and its relation with different conditioning factors. To take advantage of this analysis, we then try to leverage these 3 types of information at training time through multi-task learning, which is slightly more efficient on ASR performance prediction task.

引用

页码：49 / 76

页数：28

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