The increasing demand for learning English as a second language has led to a
growing interest in methods for automatically assessing spoken language
proficiency. Most approaches use hand-crafted features, but their efficacy
relies on their particular underlying assumptions and they risk discarding
potentially salient information about proficiency. Other approaches rely on
transcriptions produced by ASR systems which may not provide a faithful
rendition of a learner's utterance in specific scenarios (e.g., non-native
children's spontaneous speech). Furthermore, transcriptions do not yield any
information about relevant aspects such as intonation, rhythm or prosody. In
this paper, we investigate the use of wav2vec 2.0 for assessing overall and
individual aspects of proficiency on two small datasets, one of which is
publicly available. We find that this approach significantly outperforms the
BERT-based baseline system trained on ASR and manual transcriptions used for
comparison.

本研究通过研究两个小数据集，其中一个是公开的，探讨使用 wav2vec 2.0 对英语口语能力的整体和各方面进行评估的可行性，相对于基于 ASR 和手动转录训练的 BERT 基准系统，发现此方法有很大的提升。

利用 wav2vec 2.0 进行 L2 口语英语的熟练度评估

Proficiency assessment of L2 spoken English using wav2vec 2.0

Spoken language proficiency is critically important for children's growth and
personal development. Due to the limited and imbalanced educational resources
in China, elementary students barely have chances to improve their oral
language skills in classes. Verbal fluency tasks (VFTs) were invented to let
the students practice their spoken language proficiency after school. VFTs are
simple but concrete math related questions that ask students to not only report
answers but speak out the entire thinking process. In spite of the great
success of VFTs, they bring a heavy grading burden to elementary teachers. To
alleviate this problem, we develop Dolphin, a spoken language proficiency
assessment system for Chinese elementary education. Dolphin is able to
automatically evaluate both phonological fluency and semantic relevance of
students' VFT answers. We conduct a wide range of offline and online
experiments to demonstrate the effectiveness of Dolphin. In our offline
experiments, we show that Dolphin improves both phonological fluency and
semantic relevance evaluation performance when compared to state-of-the-art
baselines on real-world educational data sets. In our online A/B experiments,
we test Dolphin with 183 teachers from 2 major cities (Hangzhou and Xi'an) in
China for 10 weeks and the results show that VFT assignments grading coverage
is improved by 22\%.

本文介绍了 Dolphin，一种适用于中国小学的语言能力评估系统。该系统可以自动评估学生口语的语音流畅度和语义相关性，并在真实世界的数据集上显示了比现有基线模型更好的性能。在线实验显示，使用 Dolphin 使 VFT 作业的批改覆盖率提高了 22％。