  This study investigates whether the phonological features derived from the
Featurally Underspecified Lexicon model can be applied in text-to-speech
systems to generate native and non-native speech in English and Mandarin. We
present a mapping of ARPABET/pinyin to SAMPA/SAMPA-SC and then to phonological
features. This mapping was tested for whether it could lead to the successful
generation of native, non-native, and code-switched speech in the two
languages. We ran two experiments, one with a small dataset and one with a
larger dataset. The results supported that phonological features could be used
as a feasible input system for languages in or not in the train data, although
further investigation is needed to improve model performance. The results lend
support to FUL by presenting successfully synthesised output, and by having the
output carrying a source-language accent when synthesising a language not in
the training data. The TTS process stimulated human second language acquisition
process and thus also confirm FUL's ability to account for acquisition.
