使用微调的Wav2vec2.0和神经控制的微分方程分类器进行语音情感识别
1College of Mathematics and Statistics, Chongqing University, Chongqing, China.
PloS one
|February 20, 2025
概括
这项研究引入了一种新的语音情感识别 (SER) 模型,该模型结合了Wave2vec2.0和神经控制微分方程 (NCDEs). 拟议的方法在IEMOCAP数据集上实现了高精度和稳定性,证明了高效的学习.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 语音情感识别 (SER) 对于社交媒体和医学诊断等应用至关重要.
- 现有的SER数据集往往受到小体积和高复杂性的影响,这给建模带来了挑战.
- 音频数据的有效集成和建模仍然是SER研究中的重大障碍.
研究的目的:
- 为语音情感识别提出一个新的模型架构.
- 为了应对小型数据量和SER数据集高复杂性的挑战.
- 提高SER模型的准确性和效率.
主要方法:
- 一个混合模型架构,结合了微调的Wav2vec2.0和神经控制微分方程 (NCDEs).
- Wav2vec2.0用于提取丰富的上下文音频功能.
- 用MLP作为矢量场的NDE用于对高维时间序列特征进行分类.
主要成果:
- 该模型在IEMOCAP数据集上实现了73.37%的加权精度和74.18%的未加权精度.
- 拟议的模型显示了快速的融合,在单个培训时代内实现了良好的准确性.
- 观察到异常的模型稳定性,对加权 (0.45%) 和未加权 (0.39%) 准确度的标准偏差很低.
结论:
- 拟议的Wav2vec2.0和NDE模型为语音情感识别提供了有效的解决方案.
- 该模型的快速融合和高稳定性使其适用于实际的SER应用.
- 这种方法通过高效地建模复杂的音频数据特征来推进SER.
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