Abstract
Data augmentation is a simple and efficient technique to improve the robustness of a speech recognizer when deployed in mismatched training-test conditions. Our work, conducted during the JSALT 2015 workshop, aimed at the development of: (1) Data augmentation strategies including noising and reverberation. They were tested in combination with two approaches to signal enhancement: a carefully engineered WPE dereverberation and a learned DNN-based denoising autoencoder. (2) Proposing a novel technique for extracting an informative vector from a Sequence Summarizing Neural Network (SSNN). Similarly to i-vector extractor, the SSNN produces a “summary vector”, representing an acoustic summary of an utterance. Such vector can be used directly for adaptation, but the main usage matching the aim of this chapter is for selection of augmented training data. All techniques were tested on the AMI training set and CHiME3 test set.
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Notes
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Brno University of Technology open i-vector extractor (see http://voicebiometry.org) was used for these experiments.
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Acknowledgements
Besides the funding for the JSALT 2015 workshop, BUT researchers were supported by the Czech Ministry of Interior project no. VI20152020025, “DRAPAK,” and by the Czech Ministry of Education, Youth, and Sports from the National Program of Sustainability (NPU II) project “IT4 Innovations Excellence in Science—LQ1602.”
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Karafiát, M. et al. (2017). Training Data Augmentation and Data Selection. In: Watanabe, S., Delcroix, M., Metze, F., Hershey, J. (eds) New Era for Robust Speech Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-64680-0_10
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