用迁移学习提升YAMNet模型，让语音指令识别更准更快
Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning
- 基于预训练YAMNet，通过迁移学习微调语音指令识别模型
- 在Speech Commands数据集上达到95.28%识别准确率
- 适合语音交互系统开发者参考，提升实际应用性能
本文针对智能应用中语音指令识别系统的准确性与效率问题，利用强大的预训练YAMNet模型和迁移学习技术，提出一种有效提升语音指令识别性能的方法。通过在广泛标注的Speech Commands dataset (speech_commands_v0.01) 上进行模型适配与训练，结合数据增强与特征优化策略，显著提升了对预定义语音指令的识别能力。最终模型在测试集上达到95.28%的识别准确率，验证了先进机器学习方法在音频处理中的有效性，为该领域研究设立了新基准。
原文摘要 · Abstract (English)
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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