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The increasing role of spoken language interfaces in human-computer interaction applications has created conditions to facilitate a new area of research — namely recognizing the emotional state of the speaker through speech signals. This paper proposes a text independent method for emotion classification of speech signals used for the recognition of the emotional state of the speaker. Different feature...
Expressive speech introduces variations in the acoustic features affecting the performance of speech technology such as speaker verification systems. It is important to identify the range of emotions for which we can reliably estimate speaker verification tasks. This paper studies the performance of a speaker verification system as a function of emotions. Instead of categorical classes such as happiness...
Whispered speech, as an alternative speaking style for normal phonated (non-whispered) speech, has received little attention in speech emotion recognition. Currently, speech emotion recognition systems are exclusively designed to process normal phonated speech and can result in significantly degraded performance on whispered speech because of the fundamental differences between normal phonated speech...
Human speech is known as the information carrier because its signals can convey people's emotions, age, gender, and ethnic. Speech and emotions are interrelated where from speech, people can express their feelings. Emotional speech corpus in many languages such as English, German, Japanese, Dutch, and French are readily accessible for researchers. However, emotional speech corpus in the Malay language,...
The article presents an analysis of the possibility of recognizing speaker's emotions from speech signal in Polish language. In order to perform experiments a database containing speech recordings with emotional content was created. On its basis, extraction of features from the speech signals was performed. The most important step was to determine which of the previously extracted features were the...
Most existing Speech Emotion Recognition (SER) systems rely on turn-wise processing, which aims at recognizing emotions from complete utterances and an overly-complicated pipeline marred by many preprocessing steps and hand-engineered features. To overcome both drawbacks, we propose a real-time SER system based on end-to-end deep learning. Namely, a Deep Neural Network (DNN) that recognizes emotions...
Feature selection is very relevant for speech emotion recognition task. Still, there is no consensus on optimal feature set and classification scheme for this task. Sequential forward selection (SFS) technique for multistage emotion classification scheme is proposed in this paper. Feature sets were formed from initial collection of 6552 speech emotion features. Experimental study was performed using...
The main purpose of this paper is to determine how well can be differentiated the anxiety /fear emotion. In the analysis it is using EmoDB which contains a total number of seven emotions: happiness, fury, sadness, neutral tones, anxiety, boredom and disgust. We do not used the Romanian Database SRoL because the anxiety state is not recorded at this moment. The results are encouraging, the recognition...
For speech emotion recognition on cross-corpus, we study the problem of speaker feature adaptation. First, we discuss the existing approaches in adaptive emotional classification from speech signals. Second, the speaker feature adaptive approach is further studied in view of additive emotion feature distortion. Finally we verified our approaches using different cross-languages corpus, including German,...
Automatic emotion recognition from speech has matured close to the point where it reaches broader commercial interest. One of the last major limiting factors is the ability to deal with multilingual inputs as will be given in a real-life operating system in many if not most cases. As in real-life scenarios speech is often used mixed across languages more experience will be needed in performance effects...
Recognition of human's emotion from speech has become one of the most challenging and attractive fields of research in speech processing area. The present study aimed to detect valence of emotions, using Non-Linear Dynamic features (NLDs). NLDs are extracted from the Discrete Cosine Transform (DCT) of descriptor contours computed from Phase Space Reconstruction (PSR) of speech. These features are...
The paper is focused on automatic detection of fury emotion in audio records, using data extracted from the vocalic analysis of formants. We have studied speech prosody and voice inflexions and we recognised fury using classification algorithms applied to two databases, one with professional voices and another with normal voices, both of them recorded on the base of selected texts in Romanian language...
This paper proposes an emotion recognition system which allows recognizing a person's emotional state from speech signal. The aim of proposed solution is to improve the interaction among humans and computers. The emotion recognition system must be capable of recognizing at least six basic emotions (happiness, anger, surprise, disgust, fear, sadness) and the neutral circumstances. The proposed system...
Research in emotional speech recognition is generally focused on analysis of a set of primary emotions. However, it is clear that spontaneous speech, which is more intricate comparing to acted out utterances, carries information about emotional complexity or degree of their intensity. This research refers to the theory of Robert Plutchik, who suggested the existence of eight primary emotions. All...
Analyses the features of the time, amplitude, fundamental frequency and formant constructions involving 5 emotions as happiness, anger, surprise, sorrow and neutrality. Through speech parameters extracted from emotional database of German, this paper sums up the distribution laws of emotional features of different emotional speech signal and presents practically theoretical data for processing and...
In this paper, prosodic analysis of speech segments is performed to recognise emotions. Speech signal is segmented into words and syllables. Energy and pitch parameters are extracted from utterances, words and syllables separately to develop emotion recognition models. Eight emotions (anger, disgust, fear, happy, neutral, sad, sarcastic and surprise) of simulated emotion speech corpus, IITKGP SESC...
In this paper, simulated emotion Hindi speech corpus has been introduced for analyzing the emotions present in speech signals. The proposed database is recorded using professional artists from Gyanavani FM radio station, Varanasi, India. The speech corpus is collected by simulating eight different emotions using neutral (emotion free) text prompts. The emotions present in the database are anger, disgust,...
In this work, vowel onset points (VOPs) and pitch based spectral features are used for speech emotion classification. VOP is an anchor point from which vowel begins in a CV unit (generally a syllable). These are estimated using energy values of linear prediction (LP) residual, short time spectrum and modulation spectrum. Identification of vowel, consonant and CV transition regions of a syllable is...
Emotional speech classification is a key problem in social interaction analysis. Traditional emotional speech classification methods are completely supervised and require large amounts of labeled data. In addition, various feature sets are usually used to characterize the emotional speech signals. Therefore, we propose a new co-training algorithm based on multi-view features. More specifically, we...
This paper proposes epoch parameters extracted from LP (Linear Prediction) residual and zero frequency filtered speech signal for recognising the emotions present in speech. Instant of glottal closure within pitch period of LP residual is known as an 'epoch'. The significant excitation of vocal tract usually takes place at the instant of glottal closure. In this paper the epoch parameters namely strength...
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