  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.
