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基于音频的情绪识别使用自主监督的学习在一个工程特征空间

Peranut Nimitsurachat¹, Peter Washington²

¹Institute for Computational and Mathematical Engineering (ICME), Stanford University, Stanford, CA 94305, USA.

AI (Basel, Switzerland)

|May 8, 2024

概括

自主监督学习 (SSL) 增强了基于音频的情感识别模型,特别是当标记数据稀缺时. 这种方法通过对声学特征进行预训练来提高性能,对于容易分类的情绪证明最有效.

科学领域:

情感计算是一种情感计算.
机器学习是机器学习.
语音处理语音处理

背景情况:

从音频中识别情绪对于各种领域的交互式系统至关重要.
一个关键的挑战是,对于高性能模型来说,标记训练数据的可用性有限.
自主监督学习 (SSL) 提供了一个解决方案,通过从数据属性中学习,而无需使用广泛的标签.

研究的目的:

为了研究自我监督学习的有效性,为基于音频的情感识别进行预训练.
将SSL应用于CMU-MOSEI数据集中的编码声学特征.
与基线深度学习模型相比,评估SSL对模型性能的影响.

主要方法:

应用自主监督学习预训,使用CMU-MOSEI数据集中的编码声数据 (74个特征).
预先训练模型来预测面具声学数据时间.
使用一小组注释数据微调预训练模型.
使用平均绝对误差 (MAE) 和四类准确度评估性能,与基线进行比较.

主要成果:

自主监督学习在所有评估指标 (MAE,准确性) 中始终改善了模型性能.
当用于微调的注释数据数量很少时,性能增长最为显著.
对于快乐,悲伤和愤怒等容易分类的情绪,SSL表现出了显著的改进.
即使应用于嵌入式功能表示,SSL也提高了性能,而不仅仅是原始音频数据.

结论:

自主监督学习对于基于音频的情绪识别是非常有益的,特别是在低数据模式下.
通过利用未标记的数据,SSL有效地增强了情感计算模型.
该研究验证了SSL在编码声学特征上的实用性,为改进情绪识别系统提供了实用方法.

关键词:

情绪的分类情绪的分类情感识别情感识别情感识别自主监督学习学习转移学习转移学习

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