  The integration of natural language processing (NLP) technologies into
educational applications has shown promising results, particularly in the
language learning domain. Recently, many spoken open-domain chatbots have been
used as speaking partners, helping language learners improve their language
skills. However, one of the significant challenges is the high word-error-rate
(WER) when recognizing non-native/non-fluent speech, which interrupts
conversation flow and leads to disappointment for learners. This paper explores
the use of GPT4 for ASR error correction in conversational settings. In
addition to WER, we propose to use semantic textual similarity (STS) and next
response sensibility (NRS) metrics to evaluate the impact of error correction
models on the quality of the conversation. We find that transcriptions
corrected by GPT4 lead to higher conversation quality, despite an increase in
WER. GPT4 also outperforms standard error correction methods without the need
for in-domain training data.
