ASR systems designed for native English (L1) usually underperform on
non-native English (L2). To address this performance gap, \textbf{(i)} we
extend our previous work to investigate fine-tuning of a pre-trained wav2vec
2.0 model \cite{baevski2020wav2vec,xu2021self} under a rich set of L1 and L2
training conditions. We further \textbf{(ii)} incorporate language model
decoding in the ASR system, along with the fine-tuning method. Quantifying
gains acquired from each of these two approaches separately and an error
analysis allows us to identify different sources of improvement within our
models. We find that while the large self-trained wav2vec 2.0 may be
internalizing sufficient decoding knowledge for clean L1 speech
\cite{xu2021self}, this does not hold for L2 speech and accounts for the
utility of employing language model decoding on L2 data.

在语音识别系统中，使用 native English 训练出的模型在非母语人士的语音识别上表现欠佳。因此，本文对 pre-trained wav2vec 2.0 模型进行多种 L1 和 L2 训练条件的 fine-turning，并引入语言模型解码技术。这两种方法的量化收益及误差分析为改进模型的不同来源提供了数据，并证明了 L2 语音中引入语言模型解码技术的实用性。