Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programming
arXiv:2606.10675v1 Announce Type: new Abstract: We present a method for accurate multilingual word-level forced alignment, consisting of an alignment encoder and a learned alignment decoder. The encoder integrates two representations: one from the Massively Multilingual Speech (MMS) model and another from a self-supervised phoneme boundary detector (UnSupSeg). It learns to fuse them and to estimate word-boundary probabilities over long temporal contexts. The alignment decoder is a learned dynamic programming that combines encoder outputs with segmental features over the MMS and UnSupSeg repres
The proliferation of self-supervised learning for speech and multilingual models like MMS allows for more robust and accurate alignment methods to be developed, addressing a long-standing challenge in speech processing.
This development significantly enhances the capability for highly accurate word-level alignment in multiple languages, critical for advanced multilingual AI applications, automated translation, and improved human-computer interaction.
Multilingual speech processing systems will become more precise and efficient, reducing the need for extensive manual annotation and improving the performance of downstream tasks that rely on accurate temporal word boundaries.
- · AI developers
- · Multilingual speech tech companies
- · Language service providers
- · Researchers in NLP and speech
- · Manual annotation services
Improved multilingual automatic speech recognition and translation accuracy.
Reduced barriers for developing AI applications in less-resourced languages, leading to broader global AI adoption.
Accelerated development of universal speech interfaces and truly multilingual AI assistants, impacting global communication and commerce.
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