1,976 research outputs found
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
NLP tasks are often limited by scarcity of manually annotated data. In social
media sentiment analysis and related tasks, researchers have therefore used
binarized emoticons and specific hashtags as forms of distant supervision. Our
paper shows that by extending the distant supervision to a more diverse set of
noisy labels, the models can learn richer representations. Through emoji
prediction on a dataset of 1246 million tweets containing one of 64 common
emojis we obtain state-of-the-art performance on 8 benchmark datasets within
sentiment, emotion and sarcasm detection using a single pretrained model. Our
analyses confirm that the diversity of our emotional labels yield a performance
improvement over previous distant supervision approaches.Comment: Accepted at EMNLP 2017. Please include EMNLP in any citations. Minor
changes from the EMNLP camera-ready version. 9 pages + references and
supplementary materia
Emotion recognition from speech: tools and challenges
Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more “natural” than the acted datasets where the subjects attempt to suppress all but one emotion. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only
Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition
The face expression is the first thing we pay attention to when we want to understand a person’s state of mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research field. In this paper, because the small size of available training datasets, we propose a novel data augmentation technique that improves the performances in the recognition task. We apply geometrical transformations and build from scratch GAN models able to generate new synthetic images for each emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with different architectures. To measure the generalization ability of the models, we apply extra-database protocol approach, namely we train models on the augmented versions of training dataset and test them on two different databases. The combination of these techniques allows to reach average accuracy values of the order of 85\% for the InceptionResNetV2 model
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