用于维度语音情感识别的深层多层感知器
Deep Multilayer Perceptrons for Dimensional Speech Emotion Recognition
摘要：
现代深度学习架构通常在高性能计算设施上执行，因为输入特征的大小和模型的复杂性很大。本文提出了传统的多层感知器（MLP）与深层和小的输入大小，以解决计算需求的限制。结果表明，我们提出的深度MLP优于现代深度学习架构，即，LSTM和CNN，在相同的层数和参数值上。在IEMOCAP和MSP-IMPROV语料库上，深度MLP在说话人相关和说话人无关场景下表现出最高的性能。
英文摘要：
Modern deep learning architectures are ordinarily performed on high-performance computing facilities due to the large size of the input features and complexity of its model. This paper proposes traditional multilayer perceptrons (MLP) with deep layers and small input size to tackle that computation requirement limitation. The result shows that our proposed deep MLP outperformed modern deep learning architectures, i.e., LSTM and CNN, on the same number of layers and value of parameters. The deep MLP exhibited the highest performance on both speaker-dependent and speaker-independent scenarios on IEMOCAP and MSP-IMPROV corpus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
