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arXiv · 2210.07749

LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge

Yan Jia·Mi Hong·Jingyu Hou·Kailong Ren·Sifan Ma·Jin Wang·Fangzhen Peng·Yinglin Ji·Lin Yang·Junjie Wang
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Abstract

This paper describes LeVoice automatic speech recognition systems to track2 of intelligent cockpit speech recognition challenge 2022. Track2 is a speech recognition task without limits on the scope of model size. Our main points include deep learning based speech enhancement, text-to-speech based speech generation, training data augmentation via various techniques and speech recognition model fusion. We compared and fused the hybrid architecture and two kinds of end-to-end architecture. For end-to-end modeling, we used models based on connectionist temporal classification/attention-based encoder-decoder architecture and recurrent neural network transducer/attention-based encoder-decoder architecture. The performance of these models is evaluated with an additional language model to improve word error rates. As a result, our system achieved 10.2\% character error rate on the challenge test set data and ranked third place among the submitted systems in the challenge.

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Yan Jia, Mi Hong, Jingyu Hou, Kailong Ren, Sifan Ma, Jin Wang, Fangzhen Peng, Yinglin Ji, Lin Yang, Junjie Wang. 2022-10-14. LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge. https://arxiv.org/abs/2210.07749

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