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Speech foundation model adapted for Nepali-English code-mixed recognition

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to adapt a speech foundation model, SraVaani, for Nepali-English code-mixed speech recognition. By fine-tuning the model on a 172.8-hour corpus across six Nepali speech domains, they significantly reduced the word error rate (WER) from 48.6% to 16.3%. This approach also addressed challenges with digits and common symbols not supported by the model's tokenizer, achieving performance comparable to fine-tuned Whisper large-v3-turbo. AI

IMPACT Improves speech recognition capabilities for code-mixed languages, potentially benefiting users in multilingual communication contexts.

RANK_REASON The cluster describes a research paper published on arXiv detailing the adaptation of a speech foundation model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

Speech foundation model adapted for Nepali-English code-mixed recognition

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The cluster describes a research paper published on arXiv detailing the adaptation of a speech foundation model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jeevan Neupane, Janardan Bhetwal, Kshitiz Paudel, Anup Aryal, Bipin Bashyal ·

    Nepali-English Code-Mixed Speech Recognition: Data Preparation and Adaptation of a Speech Foundation Model

    arXiv:2611.13512v1 Announce Type: new Abstract: Nepali speakers regularly mix English words into conversation, but multilingual speech recognisers often struggle with language switches and the writing script used for English words. We study how to adapt SraVaani, a 449M-parameter…

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