CyFi-TTS: Cyclic Normalizing Flow with Fine-Grained Representation for End-to-End Text-to-Speech

Advanced end-to-end text-to-speech (TTS) systems directly generate high-quality speech. These systems demonstrate superior performance on the seen dataset from training. However, inferring speech using unseen transcripts is challenging. Usually, the generated speech tends to be mispronounced because the one-to-many problem creates an information gap between the text and speech. To address these problems, we propose a cyclic normalizing flow with fine-grained representation for end-to-end text-to-speech (CyFi-TTS), which generates natural-sounding speech by bridging the information gap. We leverage a temporal multi-resolution upsampler to progressively produce a fine-grained representation. Furthermore, we adopt a cyclic normalizing flow to produce an acoustic representation through cyclic representation learning. Experimental results reveal that CyFi-TTS directly generates speech with clear pronunciation compared to recent TTS systems. Furthermore, CyFi-TTS achieves a mean opinion score of 4.02 and a character error rate of 1.99%.