Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews

Michael Gref; Christoph Schmidt; Sven Behnke; Joachim Köhler

doi:10.1109/icme.2019.00142

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arXiv · 1908.06709

Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews

Michael Gref·Christoph Schmidt·Sven Behnke·Joachim Köhler

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Abstract

In automatic speech recognition, often little training data is available for specific challenging tasks, but training of state-of-the-art automatic speech recognition systems requires large amounts of annotated speech. To address this issue, we propose a two-staged approach to acoustic modeling that combines noise and reverberation data augmentation with transfer learning to robustly address challenges such as difficult acoustic recording conditions, spontaneous speech, and speech of elderly people. We evaluate our approach using the example of German oral history interviews, where a relative average reduction of the word error rate by 19.3% is achieved.

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Michael Gref, Christoph Schmidt, Sven Behnke, Joachim Köhler. 2019-08-19. Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews. https://doi.org/10.1109/icme.2019.00142

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