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]
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- DagsHub
- Gotit.pub
- Hindi-English
- Hugging Face
- Montreal Forced Aligner
- ScienceCast
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