Audio-conditioned phonemic and prosodic annotation for building text-to-speech models from unlabeled speech data
Journal: arXiv (Cornell University)
Year: 2024

This paper proposes an audio-conditioned phonemic and prosodic annotation model for building text-to-speech (TTS) datasets from unlabeled speech samples. For creating a TTS dataset that consists of label-speech paired data, the proposed annotation model leverages an automatic speech recognition (ASR) model to obtain phonemic and prosodic labels from unlabeled speech samples. By fine-tuning a large-scale pre-trained ASR model, we can construct the annotation model using a limited amount of label-