This paper presents an approach to emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are firstly extracted. The speech features in each selected intonation group are then extracted. With the assumption of linear mapping between feature spaces in different emotional states, a feature compensation approach is proposed to characteriz...

The problem of speech modeling for generating stressed speech using a source generator framework is addressed in this paper. In general, stress in this context refers to emotional or task induced speaking conditions. Throughout this particular study, the focus will be limited to speech under angry, loud and Lombard effect (i.e., speech produced in noise) speaking conditions. Source generator th...

Besides spoken words, speech signals also carry information about speaker gender, age, and emotional state which can be used in a variety of speech analysis applications. In this paper, a divide and conquer strategy for ensemble classification has been proposed to recognize emotions in speech. Intrinsic hierarchy in emotions has been utilized to construct an emotions tree, which assisted in bre...

Emotional speech recognition is the problem of discriminating speech samples by their emotional content. Our solution to this problem lies on three areas: signal processing, computational linguistics, and statistical learning. At signal processing and computational linguistics levels, identification and extraction of speech features is concerned; proposed in this work is a set of acoustic and l...

The man-machine relation has demanded the smart trends that machines have to react after considering the human emotional levels. The technology boost improved the machine intelligence that it gained the capability to identify human emotions at expected level. Harnessing the approaches of signal processing and pattern recognition algorithms a smart and emotions specific man-machine interaction c...

Emotion is an internal source, which can cause the speaker recognition system performance degradation by inducing extra intra-speaker vocal variability. Several enhancements have been applied to speaker recognition system under emotional speech. However, these methods suffer from the limitation of requiring the emotional speech in training or the emotion state of the speaker in testing. This pa...

This paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-seven acoustic features are extracted from the speech input. Two different classifiers Support Vector Machines (SVMs) and BP neural network are adopted to classify the emotional states. In text analysis, we use the two-step classification method to recognize ...

The CINEMO corpus of French emotional speech provides a richly annotated resource to help overcome the apparent lack of learning and testing speech material for complex, i. e. blended or mixed emotions. The protocol for its collection was dubbing selected emotional scenes from French movies. 51 speakers are contained and the total speech time amounts to 2 hours and 13 minutes and 4 k speech chu...

The study described here investigates the perceived emotional content of “affect bursts” for German. Affect bursts are defined as short emotional non-speech expressions interrupting speech. This study shows that affect bursts, presented without context, can convey a clearly identifiable emotional meaning. Affect bursts expressing ten emotions were produced by actors. After a pre-selection proce...