# Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment

arXiv: https://arxiv.org/abs/2406.19363 · HTML: https://mlsanity.com/paper/2406.19363/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2024.

## Built on by (most cited first, 21 of 21)

- [Whisper Has an Internal Word Aligner](https://mlsanity.com/paper/2509.09987/) (2025)
- [Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languages](https://mlsanity.com/paper/2506.14177/) (2025)
- [Montreal Forced Aligner and the state of speech-to-text alignment in 2026](https://mlsanity.com/paper/2606.18466/) (2026)
- [Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing](https://mlsanity.com/paper/2607.18934/) (2026)
- [DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2501.19010/) (2025)
- [Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset](https://mlsanity.com/paper/2512.17915/) (2025)
- [Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networks](https://mlsanity.com/paper/2601.18617/) (2026)
- [How Does a Deep Neural Network Look at Lexical Stress in English Words?](https://mlsanity.com/paper/2508.07229/) (2025)
- [Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programming](https://mlsanity.com/paper/2606.10675/) (2026)
- [Fully Differentiable Neural Forced Alignment via Soft Dynamic Programming](https://mlsanity.com/paper/2606.25460/) (2026)
- [SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Models](https://mlsanity.com/paper/2607.05365/) (2026)
- [Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs](https://mlsanity.com/paper/2607.06831/) (2026)
- [Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluation](https://mlsanity.com/paper/2608.28508/) (2026)
- [wav2VOT: Automatic estimation of voice onset time, closure duration, and burst realisation with wav2vec2](https://mlsanity.com/paper/2606.28857/) (2026)
- [Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approach](https://mlsanity.com/paper/2607.13158/) (2026)
- [Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin](https://mlsanity.com/paper/2607.21332/) (2026)
- [Evaluation of forced alignment of code-mixed speech: the case of Hindi-English](https://mlsanity.com/paper/2607.25581/) (2026)
- [Relative Time Intervals Representation for Word-level Timestamping with Masked Training](https://mlsanity.com/paper/2608.24041/) (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)
- [Where Words Begin and End](https://mlsanity.com/paper/2610.17782/) (2026)

## Abstract (arXiv)

Forced alignment (FA) plays a key role in speech research through the automatic time alignment of speech signals with corresponding text transcriptions. Despite the move towards end-to-end architectures for speech technology, FA is still dominantly achieved through a classic GMM-HMM acoustic model. This work directly compares alignment performance from leading automatic speech recognition (ASR) methods, WhisperX and Massively Multilingual Speech Recognition (MMS), against a Kaldi-based GMM-HMM system, the Montreal Forced Aligner (MFA). Performance was assessed on the manually aligned TIMIT and Buckeye datasets, with comparisons conducted only on words correctly recognized by WhisperX and MMS. The MFA outperformed both WhisperX and MMS, revealing a shortcoming of modern ASR systems. These findings highlight the need for advancements in forced alignment and emphasize the importance of integrating traditional expertise with modern innovation to foster progress. Index Terms: forced alignment, phoneme alignment, word alignment