Automatic Speech Recognition in German - A Detailled Error Analysis

Abstract

The amount of freely available systems for automatic speech recognition (ASR) based on neural networks is growing steadily, with equally increasingly reliable predictions. However, the evaluation of trained models is typically exclusively based on statistical metrics such as WER or CER, which do not provide any insight into the nature or impact of the errors produced when predicting transcripts from speech input. This work presents a selection of ASR model architectures that are pretrained on the German language and evaluates them on a benchmark of diverse test datasets. It identifies cross-architectural prediction errors, classifies those into categories and traces the sources of errors per category back into training data as well as other sources. Finally, it discusses solutions in order to create qualitatively better training datasets and more robust ASR systems.

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Titel	Automatic Speech Recognition in German - A Detailled Error Analysis
Medien	IEEE Coins - International Conference on Omni Layer Intelligent Systems
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Verfasser/Herausgeber	Johannes Wirth, Prof. Dr. René Peinl
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Veröffentlichungsdatum	2022-04-12
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Zitation	Wirth, Johannes; Peinl, René (2022): Automatic Speech Recognition in German - A Detailled Error Analysis. IEEE Coins - International Conference on Omni Layer Intelligent Systems.