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New research details improved forced alignment for Hindi-English code-mixed speech

By PulseAugur Editorial · [1 sources] ·

A new research paper evaluates the effectiveness of forced alignment for Hindi-English code-mixed speech, a challenging area due to linguistic variations. The study found that using bootstrapping strategies and training acoustic models on code-mixed data significantly improved alignment accuracy, reducing mean error rates by tenfold compared to monolingual approaches. The findings underscore the necessity of both principled lexicon design and specialized training data for reliable bilingual speech alignment. AI

IMPACT Improves accuracy for speech processing tools dealing with multilingual input.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research details improved forced alignment for Hindi-English code-mixed speech

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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ayushi Pandey, Pamir Gogoi, Kevin Tang ·

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

    arXiv:2607.25581v1 Announce Type: new 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 a…

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