# The Mason-Alberta Phonetic Segmenter: A forced alignment system based on deep neural networks and interpolation

arXiv: https://arxiv.org/abs/2310.15425 · HTML: https://mlsanity.com/paper/2310.15425/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)

## 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)
- [Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluation](https://mlsanity.com/paper/2608.28508/) (2026)
- [Gradient boundaries through confidence intervals for forced alignment estimates using model ensembles](https://mlsanity.com/paper/2506.01256/) (2025)
- [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)
- [Where Words Begin and End](https://mlsanity.com/paper/2610.17782/) (2026)

## Abstract (arXiv)

Forced alignment systems automatically determine boundaries between segments in speech data, given an orthographic transcription. These tools are commonplace in phonetics to facilitate the use of speech data that would be infeasible to manually transcribe and segment. In the present paper, we describe a new neural network-based forced alignment system, the Mason-Alberta Phonetic Segmenter (MAPS). The MAPS aligner serves as a testbed for two possible improvements we pursue for forced alignment systems. The first is treating the acoustic model in a forced aligner as a tagging task, rather than a classification task, motivated by the common understanding that segments in speech are not truly discrete and commonly overlap. The second is an interpolation technique to allow boundaries more precise than the common 10 ms limit in modern forced alignment systems. We compare configurations of our system to a state-of-the-art system, the Montreal Forced Aligner. The tagging approach did not generally yield improved results over the Montreal Forced Aligner. However, a system with the interpolation technique had a 27.92% increase relative to the Montreal Forced Aligner in the amount of boundaries within 10 ms of the target on the test set. We also reflect on the task and training process for acoustic modeling in forced alignment, highlighting how the output targets for these models do not match phoneticians' conception of similarity between phones and that reconciliation of this tension may require rethinking the task and output targets or how speech itself should be segmented.