Dialect-aware Semi-supervised Learning for End-to-End Multi-dialect Speech Recognition

In this paper, we propose dialect-aware semi- supervised learning for end-to-end automatic speech recognition (ASR) models considering multi-dialect speech. Some multi- domain ASR tasks require a large amount of training data containing additional information (e.g., language or dialect), whereas it is difficult to prepare such data with accurate transcriptions. Semi-supervised learning is a method of using a massive amount of untranscribed data effectively, and it can be applied to multi-domain ASR tasks to relax the missing transcriptions problem. However, semi-supervised learning has usually used generated pseudo-transcriptions only. The problem is that simply combining a multi-domain model with semi- supervised learning makes use of no additional information even though the information can be obtained. Therefore, in this paper, we focus on semi-supervised learning based on a multi-domain model that takes additional domain information into account. Since the accuracy of pseudo-transcriptions can be improved by using the multi-domain model and additional information, our proposed semi-supervised learning is expected to provide a reliable ASR model. In experiments, we performed Japanese multi-dialect ASR as one type of multi-domain ASR. From the results, a model trained with the proposed method yielded the lowest character error rate compared with other models trained with the conventional semi-supervised method.