Combining language corpora in a Japanese electromagnetic articulography database for acoustic-to-articulatory inversion

Tianfang Yan, Kikuo Maekawa, Yukiko Nota, Masayuki Hirata

Abstract: This paper presents an electromagnetic articulography database of Japanese sentences. The database includes aligned acoustics and articulatory data from seven males and three females, with a total of five recorded hours. The database is now in preparation for public release to further research in areas of acoustic-to-articulatory inversion, brain-machine interface communication systems, artificial speech synthesis, and dialect recognition. Moreover, based on this database we established an acoustic-to-articulatory inversion system using a deep, bidirectional, long short-term memory recurrent neural network structure. The results showed that, for the Japanese language, adding English corpora to the training was not beneficial for this speaker-independent model.