# BFA: Real-time Multilingual Text-to-speech Forced Alignment

arXiv: https://arxiv.org/abs/2509.23147 · HTML: https://mlsanity.com/paper/2509.23147/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2025.

## Built on by (most cited first, 5 of 5)

- [Montreal Forced Aligner and the state of speech-to-text alignment in 2026](https://mlsanity.com/paper/2606.18466/) (2026)
- [Asymmetric Hierarchical Anchoring for Robust Audio-Visual Cross-Modal Generalization](https://mlsanity.com/paper/2602.03570/) (2026)
- [TokAN: Accent Normalization Using Self-Supervised Speech Tokens](https://mlsanity.com/paper/2607.03928/) (2026)
- [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)

## Abstract (arXiv)

We present Bournemouth Forced Aligner (BFA), a system that combines a Contextless Universal Phoneme Encoder (CUPE) with a connectionist temporal classification (CTC)based decoder. BFA introduces explicit modelling of inter-phoneme gaps and silences and hierarchical decoding strategies, enabling fine-grained boundary prediction. Evaluations on TIMIT and Buckeye corpora show that BFA achieves competitive recall relative to Montreal Forced Aligner at relaxed tolerance levels, while predicting both onset and offset boundaries for richer temporal structure. BFA processes speech up to 240x faster than MFA, enabling faster than real-time alignment. This combination of speed and silence-aware alignment opens opportunities for interactive speech applications previously constrained by slow aligners.