VAE-based Phoneme Alignment Using Gradient Annealing and SSL Acoustic Features

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.