  Sequence expansion between encoder and decoder is a critical challenge in
sequence-to-sequence TTS. Attention-based methods achieve great naturalness but
suffer from unstable issues like missing and repeating phonemes, not to mention
accurate duration control. Duration-informed methods, on the contrary, seem to
easily adjust phoneme duration but show obvious degradation in speech
naturalness. This paper proposes PAMA-TTS to address the problem. It takes the
advantage of both flexible attention and explicit duration models. Based on the
monotonic attention mechanism, PAMA-TTS also leverages token duration and
relative position of a frame, especially countdown information, i.e. in how
many future frames the present phoneme will end. They help the attention to
move forward along the token sequence in a soft but reliable control.
Experimental results prove that PAMA-TTS achieves the highest naturalness,
while has on-par or even better duration controllability than the
duration-informed model.
