论文标题
使用WAV2VEC 2.0对L2口语的熟练度评估
Proficiency assessment of L2 spoken English using wav2vec 2.0
论文作者
论文摘要
对学习英语作为第二语言的需求不断增长，导致人们对自动评估口语水平的方法的兴趣日益增长。大多数方法都使用手工制作的功能，但是它们的功效依赖于其特定的基本假设，并有可能丢弃有关熟练程度的潜在显着信息。其他方法依赖于ASR系统产生的转录，这些转录可能无法在特定情况下（例如非本地儿童的自发演讲）忠实地演绎学习者的话语。此外，转录不会产生有关诸如语调，节奏或韵律等相关方面的任何信息。在本文中，我们研究了WAV2VEC 2.0在两个小数据集上评估熟练程度的整体和各个方面的使用，其中一个是公开可用的。我们发现，这种方法大大优于基于BERT的基线系统，该基线系统在ASR和用于比较的手动转录方面训练。
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.
