  Developing Text-to-Speech (TTS) systems that can synthesize natural breath is
essential for human-like voice agents but requires extensive manual annotation
of breath positions in training data. To this end, we propose a self-training
method for training a breath detection model that can automatically detect
breath positions in speech. Our method trains the model using a large speech
corpus and involves: 1) annotation of limited breath sounds utilizing a
rule-based approach, and 2) iterative augmentation of these annotations through
pseudo-labeling based on the model's predictions. Our detection model employs
Conformer blocks with down-/up-sampling layers, enabling accurate frame-wise
breath detection. We investigate its effectiveness in multi-speaker TTS using
text transcripts with detected breath marks. The results indicate that using
our proposed model for breath detection and breath mark insertion synthesizes
breath-contained speech more naturally than a baseline model.
