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.

基于模型集成的新颖 TemporalAugmenter 方法用于扩充长期和短期依赖性的时态信息捕获，从而增强时态依赖抽取的模型，同时减少预处理和特征提取的工作量，使得建立在 TemporalAugmenter 方法上的模型的能耗减少，促进绿色人工智能。此外，该方法可以简单地集成到包括工业、医疗和人机交互应用在内的各种领域。通过实证评估了语音情感识别、心电图信号和信号质量检查等三个不同复杂度和不同时间依赖特性的任务。