Abstract
Emotion Recognition is attracting the attention of the research community due to the multiple areas where it can be applied, such as in healthcare or in road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For the speech-based modality, we evaluated several transfer-learning techniques, more specifically, embedding extraction and Fine-Tuning. The best accuracy results were achieved when we fine-tuned the CNN-14 of the PANNs framework, confirming that the training was more robust when it did not start from scratch and the tasks were similar. Regarding the facial emotion recognizers, we propose a framework that consists of a pre-trained Spatial Transformer Network on saliency maps and facial images followed by a bi-LSTM with an attention mechanism. The error analysis reported that the frame-based systems could present some problems when they were used directly to solve a video-based task despite the domain adaptation, which opens a new line of research to discover new ways to correct this mismatch and take advantage of the embedded knowledge of these pre-trained models. Finally, from the combination of these two modalities with a late fusion strategy, we achieved 80.08% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. The results revealed that these modalities carry relevant information to detect users’ emotional state and their combination enables improvement of system performance.
Highlights
Emotions are present in almost every decision and moment of our lives
This difference may be explained by the dimension of the embeddings, where AlexNet embeddings have a size of 4096, the embeddings extracted from Convolutional Neural Network (CNN)-14 have a dimension of 2048, half of the size
It outperformed AlexNet results by 15.86% in the same conditions, without using Voice Activity Detector (VAD). One cause of this difference could be the nature of the training data, since AlexNet had pre-trained weights learned from images of ImageNet, whereas CNN-14 was trained using Mel spectrograms extracted from audios
Summary
Emotions are present in almost every decision and moment of our lives. recognizing emotions awakens interest, since knowing what others feel lets us interact with them more effectively. By analyzing individuals’ behavior, it is possible to detect a loss of trust or changes in emotions This capability lets that specific system, such as Conversational Systems and Embodied Conversational Agents (ECAs) [1,2], react to these events and adapt their actions to improve interactions or modify the dialogue contents, tone, or facial expressions to create a better socio-affective user experience [3]. There are systems able to recognize certain emotions (or deficits) that can help with the diagnosis of specific diseases (e.g., depressive disorders [4,5], Parkinson’s [6], etc.) and improve patients’ treatments Another relevant application of facial expression recognition is for automotive safety. Recognizing negative emotions such as stress, anger, or fatigue is crucial to avoid traffic accidents and increase the security on the road [7] on intelligent vehicles, allowing them to respond to the driver’s state
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