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In this paper, we made the emotion recognition for Romanian language using EMO-IIT database with seven emotions (joy, sadness, fury, neutral tone, anxiety, disgust and boredom). Compared to our previous studies we introduced two new emotions: disgust and boredom and a new set of sentences in order to express better the emotional states. The best recognition rate of emotions is around 75% and was obtained...
The paper presents the emotions recognition for positive and negative emotions for Romanian language. The main purpose of this study is to highlight how emotions are recognized if it is not wanted to identify with precision the expressed emotion, but the emotion in general: positive, negative or neutral. This can be useful for a human-machine interface. The positive emotions were recognized with an...
We present a study of the prosody – seen in a broader sense – that supports the theory of the interrelationship function of speech. “Pure emotions” are meant to show a relationship of the speaker with the general context. The analysis goes beyond the basic prosody, as related to pitch trajectory; namely, the analysis also aims to determine the change in higher formants. The refinement in the analysis...
The goal of the paper is to demonstrate the beneficial effects obtained by associating antioxidants in the complex therapy of the metabolic syndrome. We used dedicated software instrument for automatic classification named decision tree in order to prove that. The data taken from 60 patients were grouped in two clusters and the accuracy recognition rate obtained was of 95%.
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...
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...
In this paper, we compared the recognition rate obtained with our instruments for the emotional states (happiness, fury, sadness and neutral tone) from the SROL and from the Emo databases. The main goal of this research is to validate the instruments used for the extracting of the features vectors applied for the automatic recognition of the emotions and the results presented in our previously papers,...
The main goal of this paper is to establish the relevance of nonlinear parameters (Lyapunov exponents) in the automatic classification of emotions, for the Romanian language. The Largest Lyapunov Exponent - LLE was computed for the MFCC mel frequency cepstral coefficients and the LPCC linear prediction cepstral coefficients. The Support Vector Machine - SVM classifier provides better results than...
The paper presents the instruments implemented in SROL (Voiced Sound of Romanian Project) used for the extraction of the fundamental frequency F0 and of the formants F1–F4. In order to have a better detection of the prosodic features, we put together four methods: autocorrelation method, cepstral methods, AMDF (average magnitude difference function), HPS (harmonic product spectrum). The final value...
This paper presents an efficient segmentation method which uses simple neural network architecture. The goal was to implement an automatic annotation instrument of the vocal signal, capable to make the separation between vowel and consonant signal, respectively pauses between utterances. This instrument is used in the features extraction from the vowels areas by an emotion recognition application...
This paper presents a method for emotion recognition by using LLE - Largest Lyapunov exponent of the Mel-frequency energy bands for the Romanian language. The emotion recognition for features vectors that contains LLE is better using Support Vector Machine - SVM classifier (76.4%) than Weighted K-Nearest Neighbors - WKNN classifier (72.8%). The most efficient combination was LLE with LPC - linear...
In this study we compare the recognition accuracy of the emotions when in the feature vectors are introduced the LPC coefficients, with previous results obtained with prosodic features (F0-fundamental frequency, F1-F4-formants), respectively MFCC (Mel frequency) cepstral coefficients. From the extended sets of parameters that we introduced LPCC ((linear prediction cepstral coefficients)+LPC+PARCOR...
The automatic segmentation of the vocal signal precedes the features extraction stages, respectively the emotion recognition/classification. The extraction of the prosodic parameters as fundamental frequency (F0) and formants (F1-F4) cepstral coefficients LPCC and MFCC are made only on the vowel areas. The analysis tools from the SROL corpus are using a hybrid hierarchical system with four segmentation...
In this study, we utilized an improved version of the classical KNN algorithm which associates to each parameter from the features vectors weights according to their performance in the classification process. We obtained the recognition percents of emotions around 65–67%, for the Romanian language, on the SROL database, which are comparable with the results for other languages, with non-professional...
This study is focus on the supervised algorithm in order to classify the emotions from speech. The fuzzy-KNN classifier algorithm comparing with the classical KNN has the advantage to quantify the “strength” of the membership to a class. In the classical KNN algorithm, the decision regarding the assigning of an instance to a class was taken only based on the majority number of neighbors in a particular...
We investigate the influence of the emotions expressed by female and male speakers on the formants of the vowels in the Romanian language. The main conclusions are that the formant F4 is not significantly modified by the expressed emotion; the pitch is significantly increased for joy in all vowels; the pitch is slightly decreased under sadness, and that the change in statistic distribution is quite...
In the present study are analyzed the emotional expressiveness for two languages (German and Romanian). The emotional states are joy, fury, sadness and neutral tone. This paper aims to give an overview of what has been done in this domain based on the formantic analysis. The findings are briefly discussed.
In this study, methodological aspects of a study on /a/ vowel using formants and duration according with emotional expressiveness in Romanian language are presented. The emotional states we considered are joy, fury, sadness and neutral tone. The methodology and the WinCollections program are described. We present the contexts considered for the vowel / a /. The results obtained are shown and some...
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