面向OpenSTT的端到端ASR探索--俄语开放语音文本数据集
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset
摘要：
本文提出了一种探索的端到端的自动语音识别系统（ASR）的最大的开源俄语数据集- OpenSTT。我们评估了不同的现有端到端方法，如联合CTC/Attention，RNN-Transducer和Transformer。并与基于LF-MMI TDNN-F声学模型的强混合ASR系统进行了比较。对于三个可用的验证集（电话，YouTube和书籍），我们最好的端到端模型分别实现了34.8%，19.1%和18.1%的单词错误率（WER）。在相同条件下，hybridASR系统显示33.5%、20.9%和18.6%的WER。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
