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

Ayushi Pandey, Pamir Gogoi, Kevin Tang
7/28/2026
cs.CL

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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.

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Cite this paper

@article{pandey2026evaluation,
  title  = {Evaluation of forced alignment of code-mixed speech: the case of Hindi-English},
  author = {Ayushi Pandey and Pamir Gogoi and Kevin Tang},
  year   = {2026},
  eprint = {2607.25581},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2607.25581v1}
}

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