Abstract
In this work we examine the performance of automatic speech recognition (ASR) in industrial applications. We particularly present three experiments relating to the capturing device applied, the signal pre-processing employed, and the recognition engine used. Here, our aim was to create experimental conditions as close as possible to the envisioned application, i.e., an industrial adoption of ASR. Our results show the existence of evident dependencies between the recognition engine, the type of capturing device, and the noise type on the one side, and the complexity of the task, the present Signal-to-Noise-Ratio (SNR), and the minimum-acceptable SNR value on the other side. In summary, this work gives an overview of the capabilities and limitations of nowadays ASR systems for an application in an industrial context.
The work reported in this article has been supported by the Austrian Ministry for Transport, Innovation and Technology (bmvit).
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We note that detailed information related to the acoustic models applied by the ASR systems cannot be provided here, since these parts of the systems are meant to be used as black-boxes. We nevertheless assume that both ASR systems adopt state-of-the-art acoustic models following a DNN-HMM structure.
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Fuhrmann, F., Maly, A., Leitner, C., Graf, F. (2017). Three Experiments on the Application of Automatic Speech Recognition in Industrial Environments. In: Camelin, N., Estève, Y., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2017. Lecture Notes in Computer Science(), vol 10583. Springer, Cham. https://doi.org/10.1007/978-3-319-68456-7_9
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DOI: https://doi.org/10.1007/978-3-319-68456-7_9
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