Child’s Emotional Speech Classification by Human Across Two Languages: Russian &amp; Tamil

Lyakso, Elena E.; Frolova, Оlga; Nersisson, Ruban; Mekala, A. Mary

doi:10.1007/978-3-030-87802-3_35

Cited by 6 publications

(7 citation statements)

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“…In our first experiment on the subjective evaluation of emotional speech recognition [21], 10 native Russian experts with a mean age of 37.8 years (standard deviation ± 15.4 years) and lengthy (mean ± SD = 14.2 ± 10.3 years) professional experience as experts in the field of speech science manually recognized Happiness/Joy, Anger, Sadness, and Neutral emotional states in children's speech from the dataset consisting of 16 samples of meaningless text from "Jabberwocky" [65] and "Glokaya kuzdra" [66]). There was no preliminary training of experts.…”

Section: Results Of the Subjective Evaluation Of Emotional Speech Rec...mentioning

confidence: 99%

“…This subjective evaluation was conducted to understand how well humans identify emotional states in the acting emotional speech of Russian children. The experts used perceptual evaluation and spectrographic analysis [21] of four samples for each emotional state, with 16 samples in total. The results of this subjective evaluation are shown in Figure 2 and Tables 4 and 5.…”

Section: Results Of the Subjective Evaluation Of Emotional Speech Rec...mentioning

confidence: 99%

“…The training dataset consists of 2505 samples of the acting emotional speech of Russian children. The test set for automatic evaluation consists of 33 separate samples of acting emotional speech of Russian children used in perception tests [21]. Both classifiers were trained based on the eGeMAPS feature set [72].…”

Section: Comparison Of the Subjective Evaluation And Automatic Emotio...mentioning

confidence: 99%

“…Such a large number of speakers makes it possible to research techniques for speaker-independent emotion recognition and analysis. To validate the dataset [19,20], a subset of 33 recordings was evaluated by 10 Russian experts [21] who conducted manual tests to understand how well humans identify emotional states in the collected dataset of emotional speech of Russian speaking children. Results of manual evaluation validate the database reliability compared with other databases and the ability of native-speaking experts to recognize emotional states in children's speech in the Russian language with above chance performance.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the well-known feature sets include pitch, energy and their statistics, which are widely used in expert practice [21].…”

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confidence: 99%

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Automatic Speech Emotion Recognition of Younger School Age Children

Matveev¹,

Matveev²,

Frolova³

et al. 2022

Mathematics

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This paper introduces the extended description of a database that contains emotional speech in the Russian language of younger school age (8–12-year-old) children and describes the results of validation of the database based on classical machine learning algorithms, such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP). The validation is performed using standard procedures and scenarios of the validation similar to other well-known databases of children’s emotional acting speech. Performance evaluation of automatic multiclass recognition on four emotion classes “Neutral (Calm)—Joy—Sadness—Anger” shows the superiority of SVM performance and also MLP performance over the results of perceptual tests. Moreover, the results of automatic recognition on the test dataset which was used in the perceptual test are even better. These results prove that emotions in the database can be reliably recognized both by experts and automatically using classical machine learning algorithms such as SVM and MLP, which can be used as baselines for comparing emotion recognition systems based on more sophisticated modern machine learning methods and deep neural networks. The results also confirm that this database can be a valuable resource for researchers studying affective reactions in speech communication during child-computer interactions in the Russian language and can be used to develop various edutainment, health care, etc. applications.

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Section: Results Of the Subjective Evaluation Of Emotional Speech Rec...mentioning

confidence: 99%

Section: Results Of the Subjective Evaluation Of Emotional Speech Rec...mentioning

confidence: 99%