  Phones, the segmental units of the International Phonetic Alphabet (IPA), are
used for lexical distinctions in most human languages; Tones, the
suprasegmental units of the IPA, are used in perhaps 70%. Many previous studies
have explored cross-lingual adaptation of automatic speech recognition (ASR)
phone models, but few have explored the multilingual and cross-lingual transfer
of synchronization between phones and tones. In this paper, we test four
Connectionist Temporal Classification (CTC)-based acoustic models, differing in
the degree of synchrony they impose between phones and tones. Models are
trained and tested multilingually in three languages, then adapted and tested
cross-lingually in a fourth. Both synchronous and asynchronous models are
effective in both multilingual and cross-lingual settings. Synchronous models
achieve lower error rate in the joint phone+tone tier, but asynchronous
training results in lower tone error rate.
