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arXiv · 2006.08274

Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset

Andrei Andrusenko·Aleksandr Laptev·Ivan Medennikov
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Abstract

This paper presents an exploration of end-to-end automatic speech recognition systems (ASR) for the largest open-source Russian language data set -- OpenSTT. We evaluate different existing end-to-end approaches such as joint CTC/Attention, RNN-Transducer, and Transformer. All of them are compared with the strong hybrid ASR system based on LF-MMI TDNN-F acoustic model. For the three available validation sets (phone calls, YouTube, and books), our best end-to-end model achieves word error rate (WER) of 34.8%, 19.1%, and 18.1%, respectively. Under the same conditions, the hybridASR system demonstrates 33.5%, 20.9%, and 18.6% WER.

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BibTeXRIS

Andrei Andrusenko, Aleksandr Laptev, Ivan Medennikov. 2020-06-15. Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset. https://doi.org/10.1007/978-3-030-60276-5_4

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