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Phone-to-audio alignment without text: A Semi-supervised Approach
[article]
2022
arXiv
pre-print
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
arXiv:2110.03876v2
fatcat:kbpiirclyrayrn7wkcv4h2agfe