Application of voiced-speech variability descriptors to emotion recognition

Ślot, Krzysztof; Cichosz, Jaroslaw; Bronakowski, Lukasz

doi:10.1109/cisda.2009.5356537

Cited by 4 publications

(1 citation statement)

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“…These actors speak ten different sentences lasting from 1 to 5 seconds in the following emotional states: anger, disgust, fear, happiness, sadness, boredom, and neutral. The second dataset (dbPL) used in experiments is called Database of Polish Emotional Speech (Slot et al, 2009). It contains 240 examples of emotional speech in the Polish language recorded as monophonic with 44.1 kHz sampling rate.…”

Section: Speech Datamentioning

confidence: 99%

Archives of Acoustics

Smietanka

Maka

2021

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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.

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Section: Speech Datamentioning

confidence: 99%