Frame-Wise Breath Detection with Self-Training: An Exploration of Enhancing Breath Naturalness in Text-to-Speech

Author:

Dong Yang, Tomoki Koriyama, Yuki Saito

Keyword:

Electrical Engineering and Systems Science, Audio and Speech Processing, Audio and Speech Processing (eess.AS), Sound (cs.SD)

journal:

date:

2024-02-01 00:00:00

Abstract

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.

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