Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning

• URL: http://arxiv.org/abs/2504.19030v1
• Date: Sat, 26 Apr 2025 21:57:11 GMT
• Title: Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning
• Authors: Sidahmed Lachenani, Hamza Kheddar, Mohamed Ouldzmirli,
• Abstract summary: We adapt and train a YAMNet deep learning model to effectively detect and interpret speech commands from audio signals.<n>The final model achieved a recognition accuracy of 95.28%, underscoring the impact of advanced machine learning techniques.
• Score: 0.23408308015481666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work addresses the need for enhanced accuracy and efficiency in speech command recognition systems, a critical component for improving user interaction in various smart applications. Leveraging the robust pretrained YAMNet model and transfer learning, this study develops a method that significantly improves speech command recognition. We adapt and train a YAMNet deep learning model to effectively detect and interpret speech commands from audio signals. Using the extensively annotated Speech Commands dataset (speech_commands_v0.01), our approach demonstrates the practical application of transfer learning to accurately recognize a predefined set of speech commands. The dataset is meticulously augmented, and features are strategically extracted to boost model performance. As a result, the final model achieved a recognition accuracy of 95.28%, underscoring the impact of advanced machine learning techniques on speech command recognition. This achievement marks substantial progress in audio processing technologies and establishes a new benchmark for future research in the field.

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