用梯度提升融合语音特征，精准识别婴儿哭声以辅助心理评估

Enhancing Infant Crying Detection with Gradient Boosting for Improved Emotional and Mental Health Diagnostics

• 结合Wav2Vec与传统声学特征进行多模态输入
• 在真实数据集上分类准确率显著优于现有方法
• 适合医疗健康与儿童心理研究者参考

婴儿哭声可作为生理和情绪状态的重要指标。本文提出一种综合方法，从音频数据中检测婴儿哭声。通过将Wav2Vec与传统音频特征融合，并采用梯度提升机（Gradient Boosting Machines）进行哭声分类。在真实世界数据集上验证该方法，结果表明其性能显著优于现有技术。

原文摘要 · Abstract (English)

Infant crying can serve as a crucial indicator of various physiological and emotional states. This paper introduces a comprehensive approach detecting infant cries within audio data. We integrate Wav2Vec with traditional audio features and employ Gradient Boosting Machines for cry classification. We validate our approach on a real world dataset, demonstrating significant performance improvements over existing methods.

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