CNN achieves 91.79% accuracy for Hindi keyword spotting in speech recognition

Researchers have developed a keyword spotting system for Hindi speech recognition using a Convolutional Neural Network (CNN). The system was trained on 40,000 audio samples and utilizes Mel Frequency Cepstral Coefficients (MFCCs) as input for the CNN. Experiments with various CNN architectures demonstrated a notable accuracy of 91.79% for identifying keywords in continuous Hindi speech, emphasizing computational efficiency and user-specific customization. AI

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IMPACT Introduces a novel CNN-based approach for Hindi keyword spotting, potentially improving on-device voice command accuracy and customization.

RANK_REASON This is a research paper detailing a new method for keyword spotting in Hindi speech recognition. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Convolutional Neural Network
• CNN
• Mel Frequency Cepstral Coefficients
• MFCCs

• paper
• other