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

arXiv: https://arxiv.org/abs/2607.25581 · HTML: https://mlsanity.com/paper/2607.25581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 28 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [WhisperX: Time-Accurate Speech Transcription of Long-Form Audio](https://mlsanity.com/paper/2303.00747/) (2023)
- [L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models](https://mlsanity.com/paper/2204.08398/) (2022)
- [Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment](https://mlsanity.com/paper/2406.19363/) (2024)
- [Towards Orthographically-Informed Evaluation of Speech Recognition Systems for Indian Languages](https://mlsanity.com/paper/2603.00941/) (2026)

## Abstract (arXiv)

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.