  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.
