Abstract The ability of machines to understand human subjective emotions is an essential link realize artificial intelligence. How extract and utilize information from audio signals still a challenging task. By transforming acoustic into time-domain represented by spectrograms, advanced algorithms in the field computer vision can be applied acoustics. In this paper, we propose Speech Emotion Re...

User emotional status recognition is becoming a key feature in advanced Human Computer Interfaces (HCI). A source of information the spoken expression, which may be part interaction between human and machine. Speech emotion (SER) very active area research that involves application current machine learning neural networks tools. This ongoing review covers recent classical approaches to SER repor...

In this paper, a Mandarin speech based emotion classification method is presented. Five primary human emotions including anger, boredom, happiness, neutral and sadness are investigated. In emotion classification of speech signals, conventional features are statistics of fundamental frequency, loudness, duration and voice quality. However, the recognition accuracy of systems employing these feat...

This paper is devoted to improve automatic emotion recognition from speech by incorporating rhythm and temporal features. Research on automatic emotion recognition so far has mostly been based on applying features like MFCC’s, pitch and energy/intensity. The idea focuses on borrowing rhythm features from linguistic and phonetic analysis and applying them to the speech signal on the basis of aco...

In this paper, a Mandarin speech based emotion classification method is presented. Five primary human emotions including anger, boredom, happiness, neutral and sadness are investigated. For speech emotion recognition, we select 16 LPC coefficients, 12 LPCC components, 16 LFPC components, 16 PLP coefficients, 20 MFCC components and jitter as the basic features to form the feature vector. Two tex...

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...

To date, little research has been done in emotion classification and recognition in speech. Therefore, there is a need to discuss why this topic is interesting and present a system for classifying and recognizing emotions through speech using neural networks through this article. The proposed system will be speaker independent since a database of speech samples will be used. Various classifiers...