  Text-to-speech (TTS) synthesis is the process of producing synthesized speech
from text or phoneme input. Traditional TTS models contain multiple processing
steps and require external aligners, which provide attention alignments of
phoneme-to-frame sequences. As the complexity increases and efficiency
decreases with every additional step, there is expanding demand in modern
synthesis pipelines for end-to-end TTS with efficient internal aligners. In
this work, we propose an end-to-end text-to-waveform network with a novel
reinforcement learning based duration search method. Our proposed generator is
feed-forward and the aligner trains the agent to make optimal duration
predictions by receiving active feedback from actions taken to maximize
cumulative reward. We demonstrate accurate alignments of phoneme-to-frame
sequence generated from trained agents enhance fidelity and naturalness of
synthesized audio. Experimental results also show the superiority of our
proposed model compared to other state-of-the-art TTS models with internal and
external aligners.
