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Personalized ASR system achieves 50% error reduction for dysarthric speaker

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a personalized automatic speech recognition (ASR) system for a Czech speaker with dysarthria and a tracheostoma, whose speech is otherwise unintelligible. The system utilizes a multi-stage training pipeline, fine-tuning the Whisper Base model on various datasets including the speaker's own speech collected through an "artificial conversation" protocol. This approach achieved a 50% relative reduction in Character Error Rate compared to the baseline Whisper Base, demonstrating the feasibility of creating helpful ASR for severely impaired speech. AI

IMPACT Demonstrates potential for improved ASR accessibility for individuals with severe speech impediments.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and results. [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 →

Personalized ASR system achieves 50% error reduction for dysarthric speaker

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The cluster contains an academic paper detailing a new research methodology and results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · David Nadrchal, Monorama Swain, Florian Schmid, Gerhard Widmer, Paul Primus ·

    Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations

    arXiv:2610.03017v1 Announce Type: new Abstract: This work presents an automatic speech recognition (ASR) system personalized for a Czech speaker with a permanent tracheal stoma and severe dysarthria rendering their speech unintelligible to untrained listeners. We release a public…

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