  This paper presents an accurate phoneme alignment model that aims for speech
analysis and video content creation. We propose a variational autoencoder
(VAE)-based alignment model in which a probable path is searched using encoded
acoustic and linguistic embeddings in an unsupervised manner. Our proposed
model is based on one TTS alignment (OTA) and extended to obtain phoneme
boundaries. Specifically, we incorporate a VAE architecture to maintain
consistency between the embedding and input, apply gradient annealing to avoid
local optimum during training, and introduce a self-supervised learning
(SSL)-based acoustic-feature input and state-level linguistic unit to utilize
rich and detailed information. Experimental results show that the proposed
model generated phoneme boundaries closer to annotated ones compared with the
conventional OTA model, the CTC-based segmentation model, and the widely-used
tool MFA.
