Evaluation of forced alignment of code-mixed speech: the case of Hindi-English
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.
Keywords
Cite
@article{arxiv.2607.25581,
title = {Evaluation of forced alignment of code-mixed speech: the case of Hindi-English},
author = {Ayushi Pandey and Pamir Gogoi and Kevin Tang},
journal= {arXiv preprint arXiv:2607.25581},
year = {2026}
}