Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ground truth labels obtained by averaging or majority voting are commonly used to train automatic emotion classifiers. Hover, ground truth labels fail to encapsulate inter-annotator variability and ignore the subjectivity of emotions. ,  propose two viable approaches to model the subjectiveness of emotions by incorporating inter-annotator variability, which are soft labels and model ensembling, where each model represents an annotator. Using a deep neural network that recognizes emotions in real-time from one second windows of speech spectrograms,  demonstrate that both approaches lead to consistent improvement over using ground truth labels. It is empirically shown that the performance gain of the ensemble over the baseline model could be achieved using soft labels generated from multiple annotators.
['Haytham M. Fayek', 'Margaret Lech', 'Lawrence Cavedon']
I am writing a research paper on a idea Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels
[{"from": "human", "value": "I am writing a research paper on a idea Modeling subjectiveness in emotion recognition with deep neural networks: Ensembles vs soft labels"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Ground truth labels obtained by averaging or majority voting are commonly used to train automatic emotion classifiers. Hover, ground truth labels fail to encapsulate inter-annotator variability and ignore the subjectivity of emotions. ,  propose two viable approaches to model the subjectiveness of emotions by incorporating inter-annotator variability, which are soft labels and model ensembling, where each model represents an annotator. Using a deep neural network that recognizes emotions in real-time from one second windows of speech spectrograms,  demonstrate that both approaches lead to consistent improvement over using ground truth labels. It is empirically shown that the performance gain of the ensemble over the baseline model could be achieved using soft labels generated from multiple annotators."}]