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The task of phone-to-audio alignment has many applications in speechresearch. Here we introduce two Wav2Vec2-based models for both text-dependentand text-independent phone-to-audio alignment. The proposed Wav2Vec2-FS, asemi-supervised model, directly learns phone-to-audio alignment throughcontrastive learning and a forward sum loss, and can be coupled with apretrained phone recognizer to achieve text-independent alignment. The othermodel, Wav2Vec2-FC, is a frame classification model trained on forced alignedlabels that can both perform forced alignment and text-independentsegmentation. Evaluation results suggest that both proposed methods, even whentranscriptions are not available, generate highly close results to existingforced alignment tools. Our work presents a neural pipeline of fully automatedphone-to-audio alignment. Code and pretrained models are available athttps://github.com/lingjzhu/charsiu.

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