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    This study introduces advanced speaker-independent (SI) models for automatic speech recognition (ASR) to aid individuals with dysarthria. The new models significantly improve speech recognition accuracy for dysarthric speakers, addressing data scarcity challenges.

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    Area of Science:

    • Speech and Language Processing
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Dysarthria is a motor speech disorder impacting verbal communication.
    • Existing Automatic Speech Recognition (ASR) systems struggle with dysarthric speech due to data scarcity and speaker variability.
    • Speaker-independent (SI) approaches are crucial for developing ASR systems that assist individuals with communication impairments.

    Purpose of the Study:

    • To develop and evaluate speaker-independent (SI) models for Automatic Speech Recognition (ASR) tailored to dysarthric speech.
    • To address the challenge of data scarcity in dysarthric ASR using transfer learning and parameter-efficient fine-tuning (PEFT).
    • To establish a benchmark framework for assessing the generalizability of SI models through cross-dataset validation.

    Main Methods:

    • Developed Conformer-based SI models using a three-stage transfer-learning pipeline with selective layer freezing PEFT.
    • Pre-trained models on standard speech and progressively adapted them to two distinct dysarthric datasets (TORGO and UA-Speech).
    • Implemented a cross-dataset validation strategy to evaluate model generalizability in a realistic cross-dataset scenario.

    Main Results:

    • The proposed dysarthric SI models outperformed all baseline systems on both isolated and continuous speech recognition tasks.
    • Significant improvements were observed: 21.9% increase in word recognition accuracy for isolated speech and 18.5% reduction in word error rate for continuous speech on the TORGO dataset.
    • On UA-Speech, the optimal SI model showed a 14.6% improvement over Whisper and 28.3% over the base model for isolated speech recognition.

    Conclusions:

    • The developed dysarthric SI models demonstrate superior performance in recognizing dysarthric speech, offering a promising solution for communication assistance.
    • Cross-dataset validation revealed that models may transcribe isolated words instead of continuous speech for severe dysarthria, indicating a need for enhanced SI model generalization.
    • Further research is required to improve the generalization capabilities of SI models for continuous dysarthric speech recognition.