Ensemble modeling has been widely used to solve complex problems as it helps to improve overall performance and generalization. In this paper, we propose a novel TemporalAugmenter approach based on ensemble modeling for augmenting the temporal information capturing for long-term and short-term dependencies in data integration of two variations of recurrent neural networks in two learning streams to obtain the maximum possible temporal extraction. Thus, the proposed model augments the extraction of temporal dependencies. In addition, the proposed approach reduces the preprocessing and prior stages of feature extraction, which reduces the required energy to process the models built upon the proposed TemporalAugmenter approach, contributing towards green AI. Moreover, the proposed model can be simply integrated into various domains including industrial, medical, and human-computer interaction applications. Our proposed approach empirically evaluated the speech emotion recognition, electrocardiogram signal, and signal quality examination tasks as three different signals with varying complexity and different temporal dependency features.

翻译：集成建模已被广泛用于解决复杂问题，因为它有助于提升整体性能和泛化能力。本文提出一种基于集成建模的新型TemporalAugmenter方法，通过两个学习流中两种循环神经网络变体的数据集成，增强对长期和短期依赖关系的时域信息捕获能力，从而实现最大化的时域特征提取。因此，该模型增强了时域依赖关系的提取能力。此外，所提出的方法减少了预处理和特征提取的前置阶段，从而降低了基于该TemporalAugmenter方法构建模型所需的能量，有助于实现绿色人工智能。同时，该模型可简便地集成到工业、医疗和人机交互等多个领域的应用中。我们通过语音情感识别、心电信号处理和信号质量评估三项任务对该方法进行实证评估，这三类信号具有不同的复杂度及时域依赖特征。