Audio Feature Space Analysis for Emotion Recognition from Spoken Sentences

• Autorzy: Smietanka Lukasz , Maka Tomasz

Identyfikatory

DOI

10.24425/aoa.2021.136581

• Języki publikacji: EN

Abstrakty

EN

An analysis of low-level feature space for emotion recognition from the speech is presented. The main goal was to determine how the statistical properties computed from contours of low-level features influence the emotion recognition from speech signals. We have conducted several experiments to reduce and tune our initial feature set and to configure the classification stage. In the process of analysis of the audio feature space, we have employed the univariate feature selection using the chi-squared test. Then, in the first stage of classification, a default set of parameters was selected for every classifier. For the classifier that obtained the best results with the default settings, the hyperparameter tuning using cross-validation was exploited. In the result, we compared the classification results for two different languages to find out the difference between emotional states expressed in spoken sentences. The results show that from an initial feature set containing 3198 attributes we have obtained the dimensionality reduction about 80% using feature selection algorithm. The most dominant attributes selected at this stage based on the mel and bark frequency scales filterbanks with its variability described mainly by variance, median absolute deviation and standard and average deviations. Finally, the classification accuracy using tuned SVM classifier was equal to 72.5% and 88.27% for emotional spoken sentences in Polish and German languages, respectively.

Słowa kluczowe

EN

speech analysis; classification; emotional speech

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2021
• Tom: Vol. 46, No. 2
• Strony: 271--277
• Opis fizyczny: Bibliogr. 24 poz., rys., tab., wykr.

Twórcy

autor

Smietanka Lukasz

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

autor

Maka Tomasz

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).