Deep Learning Models for Speech Emotion Recognition
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 کاملEfficient Emotion Recognition from Speech Using Deep Learning on Spectrograms
We present a new implementation of emotion recognition from the para-lingual information in the speech, based on a deep neural network, applied directly to spectrograms. This new method achieves higher recognition accuracy compared to previously published results, while also limiting the latency. It processes the speech input in smaller segments – up to 3 seconds, and splits a longer input into...
متن کاملActive learning for dimensional speech emotion recognition
State-of-the-art dimensional speech emotion recognition systems are trained using continuously labelled instances. The data labelling process is labour intensive and time-consuming. In this paper, we propose to apply active learning to reduce according efforts: The unlabelled instances are evaluated automatically, and only the most informative ones are intelligently picked by an informativeness...
متن کاملFeature Transfer Learning for Speech Emotion Recognition
Speech Emotion Recognition (SER) has achieved some substantial progress in the past few decades since the dawn of emotion and speech research. In many aspects, various research efforts have been made in an attempt to achieve human-like emotion recognition performance in real-life settings. However, with the availability of speech data obtained from different devices and varied acquisition condi...
متن کاملRepresentation Learning for Speech Emotion Recognition
Speech emotion recognition is an important problem with applications as varied as human-computer interfaces and affective computing. Previous approaches to emotion recognition have mostly focused on extraction of carefully engineered features and have trained simple classifiers for the emotion task. There has been limited effort at representation learning for affect recognition, where features ...
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ژورنال
عنوان ژورنال: Journal of Computer Science
سال: 2018
ISSN: 1549-3636
DOI: 10.3844/jcssp.2018.1577.1587