This paper presents a novel speech emotion recognition scheme that leverages the individuality of perception. Most conventional methods simply poll multiple listeners and directly model majority decision as perceived emotion. However, perception varies with listener, which forces their single models to create complex mixtures criteria. In order mitigate this problem, we propose majority-voted f...

The success of supervised learning approaches for the classification of emotion in speech depends highly on the quality of the training data. The manual annotation of emotion speech assets is the primary way of gathering training data for emotional speech recognition. This position paper proposes the use of crowdsourcing for the rating of emotion speech assets. Recent developments in learning f...

Facial expressions and characteristics of speech are exploited intuitively by humans to infer the emotional status of their partners in communication. This paper investigates ways to extract emotion from spontaneous speech, aiming at transferring emotions to appropriate facial expressions of the speaker’s virtual representatives. Hence, this paper presents one step towards an emotional speechdr...

Research in speech emotion recognition often involves features that are extracted in lab settings or scenarios where speech quality is high. However, a great deal of communication occurs through speech codecs, which alters the speech signal and features extracted from it. The purpose of this study is to report on the performance degradation in emotion recognition systems when speech is passed t...

Speech emotion recognition is an interesting and challenging speech technology, which can be applied to broad areas. In this paper, we propose to fuse the global statistical and segmental spectral features at the decision level for speech emotion recognition. Each emotional utterance is individually scored by two recognition systems, the global statistics-based and segmental spectrum-based syst...

The growing interest in emotional speech synthesis urges effective emotion conversion techniques to be explored. This paper estimates the relevance of three speech components (spectral envelope, residual excitation and prosody) for synthesizing identifiable emotional speech, in order to be able to customize voice conversion techniques to the specific characteristics of each emotion. The analysi...

Speech and song are universal forms of vocalization that may share aspects of emotional expression. Research has focused on parallels in acoustic features, overlooking facial cues to emotion. In three experiments, we compared moving facial expressions in speech and song. In Experiment 1, vocalists spoke and sang statements each with five emotions. Vocalists exhibited emotion-dependent movements...

This talk addresses the current needs for so-called emotion in speech, but points out that the issue is better described as the expression of relationships and attitudes rather than the currently held raw (or big-six) emotional states. From an analysis of more than three years of daily conversational speech, we find the direct expression of emotion to be extremely rare, and contend that when sp...

Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech...

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...