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

By PulseAugur Editorial · [1 sources] ·

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

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]

Read on arXiv cs.AI →

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

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

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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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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Saru Bharti, Pushparaj Mani Pathak ·

    Keyword spotting using convolutional neural network for speech recognition in Hindi

    arXiv:2605.02928v1 Announce Type: cross Abstract: In this study, we investigate the application of keyword spotting (KWS) in the domain of Hindi speech recognition, utilizing a dataset comprising 40,000 audio samples. With a sampling rate of 44 kHz and an average duration of 1.9 …

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