This paper gives an overview of our activities in developing automatic speech recognition (ASR) and text-to-speech (TTS) systems for the preservation and revitalization of the Ainu language, once spoken in the Hokkaido area of Japan, and listed as "severely endangered" of extinction. With a large pretrained model, a high-performing ASR system can be trained even with five hours of speech from a few speakers. It has been used to streamline the transcription and archiving of old recordings. A TTS system is also developed and used for revitalizing the speech of old folktales whose audio is missing. It is also used to provide a reference for speaking practice for new Ainu speakers. Speech technologies are important for endangered languages because their cultures have typically been passed down orally, and our efforts will be useful for passing them on to the future.

Ainu is an unwritten language that has been spoken by Ainu people who are one of the ethnic groups in Japan. It is recognized as critically endangered by UNESCO and archiving and documentation of its language heritage is of paramount importance. Although a considerable amount of voice recordings of Ainu folklore has been produced and accumulated to save their culture, only a quite limited parts of them are transcribed so far. Thus, we started a project of automatic speech recognition (ASR) for the Ainu language in order to contribute to the development of annotated language archives. In this paper, we report speech corpus development and the structure and performance of end-to-end ASR for Ainu. We investigated four modeling units (phone, syllable, word piece, and word) and found that the syllable-based model performed best in terms of both word and phone recognition accuracy, which were about 60% and over 85% respectively in speaker-open condition. Furthermore, word and phone accuracy of 80% and 90% has been achieved in a speaker-closed setting. We also found out that a multilingual ASR training with additional speech corpora of English and Japanese further improves the speaker-open test accuracy.