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    Wyświetlanie 1-3 z 3
    Tytuł:
    Analysis of Features and Classifiers in Emotion Recognition Systems : Case Study of Slavic Languages
    Autorzy:
    Nedeljković, Željko
    Milošević, Milana
    Ðurović, Željko
    Powiązania:
    https://bibliotekanauki.pl/articles/176678.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    emotion recognition
    speech processing
    classification algorithms
    Opis:
    Today’s human-computer interaction systems have a broad variety of applications in which automatic human emotion recognition is of great interest. Literature contains many different, more or less successful forms of these systems. This work emerged as an attempt to clarify which speech features are the most informative, which classification structure is the most convenient for this type of tasks, and the degree to which the results are influenced by database size, quality and cultural characteristic of a language. The research is presented as the case study on Slavic languages.
    Źródło:
    Archives of Acoustics; 2020, 45, 1; 129-140
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Acoustic Methods in Identifying Symptoms of Emotional States
    Autorzy:
    Piątek, Zuzanna
    Kłaczyński, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/1953482.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    emotion recognition
    speech signal processing
    clustering analysis
    Sammon mapping
    Opis:
    The study investigates the use of speech signal to recognise speakers’ emotional states. The introduction includes the definition and categorization of emotions, including facial expressions, speech and physiological signals. For the purpose of this work, a proprietary resource of emotionally-marked speech recordings was created. The collected recordings come from the media, including live journalistic broadcasts, which show spontaneous emotional reactions to real-time stimuli. For the purpose of signal speech analysis, a specific script was written in Python. Its algorithm includes the parameterization of speech recordings and determination of features correlated with emotional content in speech. After the parametrization process, data clustering was performed to allows for the grouping of feature vectors for speakers into greater collections which imitate specific emotional states. Using the t-Student test for dependent samples, some descriptors were distinguished, which identified significant differences in the values of features between emotional states. Some potential applications for this research were proposed, as well as other development directions for future studies of the topic.
    Źródło:
    Archives of Acoustics; 2021, 46, 2; 259-269
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech Emotion Recognition Based on Voice Fundamental Frequency
    Autorzy:
    Dimitrova-Grekow, Teodora
    Klis, Aneta
    Igras-Cybulska, Magdalena
    Powiązania:
    https://bibliotekanauki.pl/articles/177227.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    emotion recognition
    speech signal analysis
    voice analysis
    fundamental frequency
    speech corpora
    Opis:
    The human voice is one of the basic means of communication, thanks to which one also can easily convey the emotional state. This paper presents experiments on emotion recognition in human speech based on the fundamental frequency. AGH Emotional Speech Corpus was used. This database consists of audio samples of seven emotions acted by 12 different speakers (6 female and 6 male). We explored phrases of all the emotions – all together and in various combinations. Fast Fourier Transformation and magnitude spectrum analysis were applied to extract the fundamental tone out of the speech audio samples. After extraction of several statistical features of the fundamental frequency, we studied if they carry information on the emotional state of the speaker applying different AI methods. Analysis of the outcome data was conducted with classifiers: K-Nearest Neighbours with local induction, Random Forest, Bagging, JRip, and Random Subspace Method from algorithms collection for data mining WEKA. The results prove that the fundamental frequency is a prospective choice for further experiments.
    Źródło:
    Archives of Acoustics; 2019, 44, 2; 277-286
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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