长短期记忆(LSTM)是一种用于深度学习领域的人工递归神经网络(RNN)架构。与标准前馈神经网络不同,LSTM具有反馈连接。它不仅可以处理单个数据点(例如图像),而且可以处理整个数据序列(例如语音或视频)。例如,LSTM适用于诸如未分段的连接手写识别,语音识别和网络流量或IDS(入侵检测系统)中的异常检测之类的任务。常见的LSTM单元由单元,输入门,输出门和忘记门组成。单元会记住任意时间间隔内的值,并且三个门控制着进出单元的信息流。LSTM网络非常适合基于时间序列数据进行分类,处理和做出预测,因为时间序列中重要事件之间可能存在未知持续时间的滞后。开发LSTM是为了解决训练传统RNN时可能遇到的梯度消失问题。与缝隙长度相对不敏感是LSTM在众多应用中优于RNN,隐马尔可夫模型和其他序列学习方法的优势。

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Emotion is an inherently subjective psychophysiological human-state and to produce an agreed-upon representation (gold standard) for continuous emotion requires a time-consuming and costly training procedure of multiple human annotators. There is strong evidence in the literature that physiological signals are sufficient objective markers for states of emotion, particularly arousal. In this contribution, we utilise a dataset which includes continuous emotion and physiological signals - Heartbeats per Minute (BPM), Electrodermal Activity (EDA), and Respiration-rate - captured during a stress inducing scenario (Trier Social Stress Test). We utilise a Long Short-Term Memory, Recurrent Neural Network to explore the benefit of fusing these physiological signals with arousal as the target, learning from various audio, video, and textual based features. We utilise the state-of-the-art MuSe-Toolbox to consider both annotation delay and inter-rater agreement weighting when fusing the target signals. An improvement in Concordance Correlation Coefficient (CCC) is seen across features sets when fusing EDA with arousal, compared to the arousal only gold standard results. Additionally, BERT-based textual features' results improved for arousal plus all physiological signals, obtaining up to .3344 CCC compared to .2118 CCC for arousal only. Multimodal fusion also improves overall CCC with audio plus video features obtaining up to .6157 CCC to recognize arousal plus EDA and BPM.

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