Deep Emotion
نویسندگان
چکیده
منابع مشابه
Speech Emotion Recognition Using Scalogram Based Deep Structure
Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...
متن کاملEmotion Recognition Using Multimodal Deep Learning
To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models with SEED and DEAP datasets to recognize different kinds of emotions. We demonstrate that high level representation features extracted by the Bimodal Deep AutoEncoder (BDAE) are effective for e...
متن کاملEmotion Recognition with Deep-Belief Networks
For our CS229 project, we studied the problem of reliable computerized emotion recognition in images of human faces. First, we performed a preliminary exploration using SVM classifiers, and then developed an approach based on Deep Belief Nets. Deep Belief Nets, or DBNs, are probabilistic generative models composed of multiple layers of stochastic latent variables, where each “building block” la...
متن کاملSpoken Emotion Recognition Using Deep Learning
Spoken emotion recognition is a multidisciplinary research area that has received increasing attention over the last few years. In this paper, restricted Boltzmann machines and deep belief networks are used to classify emotions in speech. The motivation lies in the recent success reported using these alternative techniques in speech processing and speech recognition. This classifier is compared...
متن کاملMultimodal Emotion Recognition Using Deep Neural Networks
The change of emotions is a temporal dependent process. In this paper, a Bimodal-LSTM model is introduced to take temporal information into account for emotion recognition with multimodal signals. We extend the implementation of denoising autoencoders and adopt the Bimodal Deep Denoising AutoEncoder modal. Both models are evaluated on a public dataset, SEED, using EEG features and eye movement ...
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ژورنال
عنوان ژورنال: Journal of Japan Institute of Light Metals
سال: 1990
ISSN: 0451-5994
DOI: 10.2464/jilm.40.484