Montreal Forced Aligner and the state of speech-to-text alignment in 2026
The Montreal Forced Aligner (MFA) was released in 2016 and has since become the most widely used tool for forced alignment in research and industry. MFA 3.0 achieves state-of-the-art or near state-of-the-art performance across four benchmark datasets with mean boundary errors below 15 ms. Adaptation and cross-language remapping are effective for languages outside MFA's training distribution, and pronunciation probability modeling and phonological rules provide gains in specific conditions.
[2606.18466] Montreal Forced Aligner and the state of speech-to-text alignment in 2026
[Submitted on 16 Jun 2026]
Title:Montreal Forced Aligner and the state of speech-to-text alignment in 2026
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Abstract:The Montreal Forced Aligner (MFA) was released in 2016 and has since become the most widely used tool for forced alignment in research and industry. In the decade since, MFA has undergone substantial development, including expanded coverage across more languages and dialects using larger open-source datasets, harmonized IPA dictionaries, model adaptation, cross-language phone remapping, and support utilities. This paper documents MFA 3.0's developments since version 1.0 and evaluates MFA's performance across English, Japanese, and Korean, benchmarked against classic and neural forced aligners. MFA 3.0 achieves state-of-the-art or near state-of-the-art performance across all four benchmark datasets with mean boundary errors below 15 ms. Adaptation and cross-language remapping are effective for languages outside MFA's training distribution, and pronunciation probability modeling and phonological rules provide gains in specific conditions.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.18466 [cs.CL]
(or arXiv:2606.18466v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.18466
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Michael McAuliffe [view email] [v1] Tue, 16 Jun 2026 20:18:09 UTC (66 KB)
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