mlsanityData to 14 Oct 2026

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

arXiv 2406.19363 ↗First posted 27 Jun 2024eess.AScited by 21

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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

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

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Citing papers per year: 2025: 5; 2026: 16.

21 papers.

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  1. 2025Whisper Has an Internal Word Alignercited by 7
  2. 2025Can we train ASR systems on Code-switch without real code-switch data? Case study for Singapore's languagescited by 5
  3. 2026Montreal Forced Aligner and the state of speech-to-text alignment in 2026cited by 3
  4. 2026Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timingcited by 3
  5. 2025DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognitioncited by 2
  6. 2025Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Datasetcited by 2
  7. 2026Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networkscited by 2
  8. 2025How Does a Deep Neural Network Look at Lexical Stress in English Words?cited by 1
  9. 2026Multilingual Word-Level Forced Alignment with Self-Supervised Representations and Learned Dynamic Programmingcited by 1
  10. 2026Fully Differentiable Neural Forced Alignment via Soft Dynamic Programmingcited by 1
  11. 2026SPEARBench: A Benchmark for Naturalness Evaluation in Streaming Speech-to-Speech Language Modelscited by 1
  12. 2026Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMscited by 1
9 more
  1. 2026Phoneme- and Word-Level Metrics Using Self-Supervised Speech Representations for Forced Alignment Evaluationcited by 1
  2. 2026wav2VOT: Automatic estimation of voice onset time, closure duration, and burst realisation with wav2vec2cited by 0
  3. 2026Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approachcited by 0
  4. 2026Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarincited by 0
  5. 2026Evaluation of forced alignment of code-mixed speech: the case of Hindi-Englishcited by 0
  6. 2026Relative Time Intervals Representation for Word-level Timestamping with Masked Trainingcited by 0
  7. 2026FA-Bench: A Benchmark for Phone- and Word-Level Timestamp Accuracy in Forced Alignment and ASR on Clean and Noisy Speechcited by 0
  8. 2026FuseAlign: Forced Alignment in the Wildcited by 0
  9. 2026Where Words Begin and Endcited by 0