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Evaluation of forced alignment of code-mixed speech: the case of Hindi-English

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Computer Science > Computation and Language

arXiv:2607.25581 (cs)
[Submitted on 28 Jul 2026]

Title:Evaluation of forced alignment of code-mixed speech: the case of Hindi-English

View a PDF of the paper titled Evaluation of forced alignment of code-mixed speech: the case of Hindi-English, by Ayushi Pandey and 2 other authors
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Abstract:Code-mixed speech poses unique challenges to forced alignment: expanded inventories, orthographic errors, and speaker variation. We evaluate forced alignment of Hindi-English code-mixed speech using the Montreal Forced Aligner. We address 2 problems: (1) free variation involving native vs non-native pairs and (2) phonemic boundary detection for mid-utterance English words. Bootstrapping strategies substantially outperform unmodified lexicons. Acoustic models trained on sentence-level code-mixed data achieve a mean error of 4.15ms, ie. ten times lower than monolingual Hindi (38.18ms) or isolated English (37.58ms) alternatives. Principled lexicon design and code-mixed training data are both essential for reliable alignment of bilingual speech.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2607.25581 [cs.CL]
  (or arXiv:2607.25581v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2607.25581
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Pamir Gogoi [view email]
[v1] Tue, 28 Jul 2026 11:08:05 UTC (576 KB)
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