  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.

Audio and Speech Processing;Sound