Multimodal emotional state recognition using sequence-dependent deep hierarchical features
نویسندگان
چکیده
منابع مشابه
Multimodal Emotion 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 from multiple physiological signals. For unimodal enhancement task, we indicate that the best recognition accuracy of 82.11% on SEED dataset is achieved with shared representations generated by...
متن کاملText-Dependent Speaker Recognition Using Emotional Features and Neural Networks
This paper deals with a novel feature extraction method for text dependent speaker recognition. Four female speakers were used to create a text –dependent database for Malayalam (one of the south Indian languages). Discrete Wavelet Transform was used for feature extraction and artificial neural network was used for machine intelligence. In this work we used emotional features for speaker recogn...
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 ...
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 کاملFreehand Sketch Recognition Using Deep Features
Freehand sketches often contain sparse visual detail. In spite of the sparsity, they are easily and consistently recognized by humans across cultures, languages and age groups. Therefore, analyzing such sparse sketches can aid our understanding of the neurocognitive processes involved in visual representation and recognition. In the recent past, Convolutional Neural Networks (CNNs) have emerged...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neural Networks
سال: 2015
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2015.09.009