New Multilingual Word-Level Forced Alignment Method Outperforms Existing Approaches
Researchers have developed a new method for word-level forced alignment in speech that combines representations from the Massively Multilingual Speech model and a self-supervised phoneme boundary detector. The approach uses a learned dynamic programming decoder and was trained on TIMIT and Buckeye datasets. The method shows potential to scale across 1100+ languages without requiring additional training.
A new multilingual word-level forced alignment method has been presented that integrates two key representations: one from the Massively Multilingual Speech (MMS) model and another from a self-supervised phoneme boundary detector called UnSupSeg. The system consists of an alignment encoder that learns to fuse these representations and estimate word-boundary probabilities over long temporal contexts, combined with a learned dynamic programming decoder that infers final word boundaries. When tested on TIMIT and Buckeye datasets, the proposed approach outperformed existing methods including Montreal Forced Aligner (MFA) and MMS-based alignment. On unseen languages including Dutch, German, and Hebrew, the model achieved performance consistently better than or comparable to existing alignment approaches, suggesting it could scale effectively to the 1100+ languages supported by MMS without requiring additional training.
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- arXiv cs.CLCenter
Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programming
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