This paper describes the THUEE team's speech recognition system for the IARPA
Open Automatic Speech Recognition Challenge (OpenASR21), with further
experiment explorations. We achieve outstanding results under both the
Constrained and Constrained-plus training conditions. For the Constrained
training condition, we construct our basic ASR system based on the standard
hybrid architecture. To alleviate the Out-Of-Vocabulary (OOV) problem, we
extend the pronunciation lexicon using Grapheme-to-Phoneme (G2P) techniques for
both OOV and potential new words. Standard acoustic model structures such as
CNN-TDNN-F and CNN-TDNN-F-A are adopted. In addition, multiple data
augmentation techniques are applied. For the Constrained-plus training
condition, we use the self-supervised learning framework wav2vec2.0. We
experiment with various fine-tuning techniques with the Connectionist Temporal
Classification (CTC) criterion on top of the publicly available pre-trained
model XLSR-53. We find that the frontend feature extractor plays an important
role when applying the wav2vec2.0 pre-trained model to the encoder-decoder
based CTC/Attention ASR architecture. Extra improvements can be achieved by
using the CTC model finetuned in the target language as the frontend feature
extractor.

该论文介绍了 THUEE 团队为 IARPA OpenASR21 挑战赛开发的语音识别系统，包括使用基于混合体系结构的 ASR 系统、使用 Grapheme-to-Phoneme 技术扩展发音词典来缓解词汇外问题、使用 self-supervised 学习框架 wav2vec2.0 等技术来提高识别准确度等。