Abstract

(English Only) Recent text-to-speech models have been requested to synthesize natural speech from language-mixed sentences because they are commonly used in real-world applications. However, most models do not consider transliterated words as input. When generating speech from transliterated text, it is not always natural to pronounce transliterated words as they are written, such as in the case of song titles. To address this issue, we introduce FACTSpeech, a system that can synthesize natural speech from transliterated text while allowing users to control the pronunciation between native and literal languages. Specifically, we propose a new language shift embedding to control the pronunciation of input text between native or literal pronunciation. Moreover, we leverage conditional instance normalization to improve pronunciation while preserving the speaker's identity. The experimental results show that FACTSpeech generates native speech even from the sentences of transliterated form.