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arXiv · 1807.00868

Exploring End-to-End Techniques for Low-Resource Speech Recognition

Vladimir Bataev·Maxim Korenevsky·Ivan Medennikov·Alexander Zatvornitskiy
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Abstract

In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 hours). We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU. Different features and normalization techniques are compared as well. We also proposed CTC-loss modification using segmentation during training, which leads to improvement while decoding with small beam size. Our best model achieved word error rate of 45.8%, which is the best reported result for end-to-end systems using in-domain data for this task, according to our knowledge.

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BibTeXRIS

Vladimir Bataev, Maxim Korenevsky, Ivan Medennikov, Alexander Zatvornitskiy. 2018-07-02. Exploring End-to-End Techniques for Low-Resource Speech Recognition. https://arxiv.org/abs/1807.00868

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