  Recent popular decoder-only text-to-speech models are known for their ability
of generating natural-sounding speech. However, such models sometimes suffer
from word skipping and repeating due to the lack of explicit monotonic
alignment constraints. In this paper, we notice from the attention maps that
some particular attention heads of the decoder-only model indicate the
alignments between speech and text. We call the attention maps of those heads
Alignment-Emerged Attention Maps (AEAMs). Based on this discovery, we propose a
novel inference method without altering the training process, named
Attention-Constrained Inference (ACI), to facilitate monotonic synthesis. It
first identifies AEAMs using the Attention Sweeping algorithm and then applies
constraining masks on AEAMs. Our experimental results on decoder-only TTS model
VALL-E show that the WER of synthesized speech is reduced by up to 20.5%
relatively with ACI while the naturalness and speaker similarity are
comparable.
