标题： DeepEmoNet：构建用于人类言语中自动情感识别的机器学习模型 显示英文标题

标题： DeepEmoNet: Building Machine Learning Models for Automatic Emotion Recognition in Human Speeches

摘要： 语音情感识别(SER)一直是口语语言处理研究中的一个挑战性问题，因为不清楚人类情感如何与声音的各种成分如音高、响度和能量相关联。 本文旨在使用机器学习来解决这个问题。 特别是，我们使用SVM、LTSM和CNN构建了几个机器学习模型来对人声中的情感进行分类。 此外，通过利用迁移学习和数据增强，我们高效地训练了模型，在相对较小的数据集上获得了良好的性能。 我们最好的模型是一个ResNet34网络，其准确率为$66.7\%$，F1得分为$0.631$。 显示英文摘要

摘要： Speech emotion recognition (SER) has been a challenging problem in spoken language processing research, because it is unclear how human emotions are connected to various components of sounds such as pitch, loudness, and energy. This paper aims to tackle this problem using machine learning. Particularly, we built several machine learning models using SVMs, LTSMs, and CNNs to classify emotions in human speeches. In addition, by leveraging transfer learning and data augmentation, we efficiently trained our models to attain decent performances on a relatively small dataset. Our best model was a ResNet34 network, which achieved an accuracy of $66.7\%$ and an F1 score of $0.631$.