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Phone-to-audio alignment without text: A Semi-supervised Approach

2021-10-08 03:30:24
Jian Zhu, Cong Zhang, David Jurgens
arXiv_CL
           
Abstract

The task of phone-to-audio alignment has many applications in speech research. Here we introduce two Wav2Vec2-based models for both text-dependent and text-independent phone-to-audio alignment. The proposed Wav2Vec2-FS, a semi-supervised model, directly learns phone-to-audio alignment through contrastive learning and a forward sum loss, and can be coupled with a pretrained phone recognizer to achieve text-independent alignment. The other model, Wav2Vec2-FC, is a frame classification model trained on forced aligned labels that can both perform forced alignment and text-independent segmentation. Evaluation results suggest that both proposed methods, even when transcriptions are not available, generate highly close results to existing forced alignment tools. Our work presents a neural pipeline of fully automated phone-to-audio alignment. Code and pretrained models are available at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2110.03876

PDF

https://arxiv.org/pdf/2110.03876.pdf

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