# Montreal Forced Aligner and the state of speech-to-text alignment in 2026

arXiv: https://arxiv.org/abs/2606.18466 · HTML: https://mlsanity.com/paper/2606.18466/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 65 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [PyThaiNLP: Thai Natural Language Processing in Python](https://mlsanity.com/paper/2312.04649/) (2023)
- [Phone-to-audio alignment without text: A Semi-supervised Approach](https://mlsanity.com/paper/2110.03876/) (2021)
- [Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment](https://mlsanity.com/paper/2406.19363/) (2024)
- [PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation](https://mlsanity.com/paper/1906.11455/) (2019)
- [The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation](https://mlsanity.com/paper/2310.15425/) (2023)
- [BFA: Real-time Multilingual Text-to-speech Forced Alignment](https://mlsanity.com/paper/2509.23147/) (2025)
- [Multilingual MFA: Forced Alignment on Low-Resource Related Languages](https://mlsanity.com/paper/2504.07315/) (2025)

## Built on by (most cited first, 3 of 3)

- [X2-NativeCursor: Native-Token Text Progress Tracking for Incremental-Text Streaming Codec TTS](https://mlsanity.com/paper/2609.09677/) (2026)
- [FA-Bench: A Benchmark for Phone- and Word-Level Timestamp Accuracy in Forced Alignment and ASR on Clean and Noisy Speech](https://mlsanity.com/paper/2609.32396/) (2026)
- [FuseAlign: Forced Alignment in the Wild](https://mlsanity.com/paper/2609.33650/) (2026)

## Abstract (arXiv)

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.