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

• URL: http://arxiv.org/abs/2006.08274v2
• Date: Sun, 26 Jul 2020 20:21:09 GMT
• Title: Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset
• Authors: Andrei Andrusenko, Aleksandr Laptev, Ivan Medennikov
• Abstract summary: 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. 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.
• Score: 73.66530509749305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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