Speech emotion recognition is an important issue in the development of human-computer interactions. In this paper a series of novel robust features for speech emotion recognition is proposed. Those features, which derived from the Hilbert-Huang transform (HHT) and Teager energy operator (TEO), have the characteristics of multi-resolution, self-adaptability and high precision of distinguish abil...

This paper proposes three corpora of emotional speech in Japanese that maximize the expression of each emotion (expressing joy, anger, and sadness) for use with CHATR, the concatenative speech synthesis system being developed at ATR. A perceptual experiment was conducted using the synthesized speech generated from each emotion corpus and the results proved to be significantly identifiable. Auth...

Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing the performance of a classifier. For speech emotion recognition tasks, generating effective features is crucial. Currently, handcrafted features are mostly used for speech emotion recognition, however, features learned automatically using deep learning h...

Numerous examinations are performed related to automatic emotion recognition and speech detection in the Laboratory of Speech Acoustics. This article reviews results achieved for automatic emotion recognition experiments on spontaneous speech databases on the base of the acoustical information only. Different acoustic parameters were compared for the acoustical preprocessing, and Support Vector...

Speech Emotion Recognition (SER) makes it possible for machines to perceive affective information. Our previous research differed from conventional SER endeavours in that focused on recognising unseen emotions speech autonomously through machine learning. Such a step would enable the automatic leaning of unknown emerging emotional states. This type learning framework, however, still relied manu...

Speech emotion recognition is a crucial work direction in speech recognition. To increase the performance of detection, researchers have worked relentlessly to improve data augmentation, feature extraction, and pattern formation. address concerns limited resources model training overfitting, A-CapsNet, neural network based on augmentation methodologies, proposed this research. In order solve is...

This paper proposes a computational emotion formation model and attempt to apply the model for speech communication. As the speech communication plays an essential part of our daily life, the utilization of the speech sound will be expected to make human-robot communication smooth. The proposed model forms the state of emotion based on the prosodic components because the emotional aspects of hu...

The study of emotions in human-computer interaction is a growing research area. Focusing on automatic emotion recognition, work is being performed in order to achieve good results particularly in speech and facial gesture recognition. In this paper we present a study performed to analyze different machine learning techniques validity in automatic speech emotion recognition area. Using a bilingu...

Emotion in speech is an important and challenging research area. Addition or understanding of emotions from speech is challenging. But an equally difficult task is to identify the intended emotion from an audio or speech. Understanding emotions is important not only in itself as a research area, but also, for adding emotions to synthesized speech. Evaluating synthesized speech with emotions can...

In the era of data explosion, speech emotion plays crucial commercial significance. Emotion recognition in speech encompasses a gamut of techniques starting from mechanical recording of audio signal to complex modeling of extracted patterns. Most challenging part of this research purview is to classify the emotion of the speech purely based on the physical characteristics of the audio signal in...