[2Yin5] インタラクティブ2

[2Yin5-04] Generation and evaluation of pseudo error data for error correction of Japanese speech recognition

〇Masakazu Sugiyama¹, Ryoma Yoshimura², Yuta Tomomatsu¹, Mamoru Komachi² (1.AI Shift, Inc., 2.Tokyo Metropolitan University)

Keywords:grammatical error correction, speech recognition, pseudo error data

In recent years, the performance of speech recognition and speech synthesis has improved, and automatic voice response services using them have begun to be widely provided. In that service, the accuracy of speech recognition is an important factor that is directly linked to the quality of service, but the accuracy of speech recognition is not perfect even though the performance has been improved. So, we consider correcting speech recognition errors, in the same way as grammatical error correction. The performance of grammatical error correction has improved dramatically due to the rise of deep learning methods using language models pre-trained with a huge corpora, but there is no huge Japanese speech recognition error corpus. Therefore, we analyzed the tendency of errors from a small Japanese speech recognition corpus, formulated error assignment rules, and applied the rule to a huge Japanese corpus to automatically create a pseudo speech recognition error corpus.
In this study, we perform an error correction experiment by Transformer using pseudo error corpora created under multiple settings for pre-training, and evaluate the effect of corpus creation settings on accuracy.