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.

翻译：这项研究调查了从Featalilly under Featolly under declater Lexicon 模型中产生的声学特征是否可以应用于文字对语音系统,以生成英语和普通话的本地和非本地语言演讲。我们向SAMPA/SAMPA-SC提供了ARABET/pinyin的地图,然后向声学特征提供了地图。这一绘图测试了它是否能成功生成两种语言的本地、非本地和代码转换的语音。我们进行了两个实验,一个是小数据集,一个是大数据集。结果支持将声学特征用作火车数据中或非火车数据中语言的可行输入系统,尽管需要进一步研究以改善模型性能。这些结果通过成功合成产出和在综合培训数据中不使用一种语言时使产出带有一种源语言的口音来帮助实现。TTS过程刺激了人类第二语言获取过程,从而也证实了FUCT的获取能力。