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New SBPN model boosts Nigerian language ASR via knowledge distillation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new multilingual Automatic Speech Recognition (ASR) framework called Sometin Beta Pass Notin (SBPN) to improve performance for Nigerian languages. The framework uses a two-stage knowledge distillation process, first from monolingual models and then through iterative self-improvement with pseudo-labeled data. This approach achieved an average 29% relative Word Error Rate reduction over baselines and outperforms existing state-of-the-art multilingual models on benchmarks like Common Voice and Fleurs. SBPN is released as open foundation models in two sizes, aiming to provide crucial ASR resources for the region. AI

IMPACT Provides open-source ASR models for under-resourced Nigerian languages, potentially enabling new applications and research.

RANK_REASON Publication of an academic paper detailing a new model and methodology. [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 SBPN model boosts Nigerian language ASR via knowledge distillation

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Publication of an academic paper detailing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sewade Ogun ·

    Sometin Beta Pass Notin (SBPN): Improving Multilingual ASR for Nigerian Languages via Knowledge Distillation

    Although modern multilingual Automatic Speech Recognition (ASR) systems support several Nigerian languages, their performance consistently lags behind high-resource languages like English and French. Nigerian languages present unique modelling hurdles, including acute data scarci…

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