ANALYSIS OF PROSODIC PARAMETERS OF EMOTIONALLY COLORED SPEECH
This paper presents a study of prosodic parameters of emotionally colored speech in the Russian language. The aim of the study is to identify the most informative acoustic features that allow distinguishing the emotional state of a speaker. The experimental data consisted of audio recordings from the Dusha emotional speech dataset, including four emotional states: anger, joy, sadness, and neutral speech. In total, 240 audio recordings of both male and female speakers were analyzed.
The study focused on extracting and analyzing prosodic characteristics of speech signals, including pitch-related, energy, temporal, and phonation features. A combination of statistical analysis and machine learning methods was applied, including correlation analysis, feature importance estimation using the Random Forest algorithm, and Principal Component Analysis (PCA).
The experimental results demonstrate that energy and pitch-related characteristics of speech are the most informative features for emotion recognition. In particular, mean signal energy, variability of the fundamental frequency, speech rate, and mean F0 showed the highest contribution to emotion classification. The analysis allowed identifying a compact feature space and revealing characteristic acoustic profiles for different emotional states. The obtained results can be used in the development of automatic speech emotion recognition systems and intelligent speech-based human–computer interaction technologies.
Balabanova T.N., Belov A.S., Pashkov A.S., Mamatov E.M. Analysis of Prosodic Parameters of Emotionally Colored Speech // Research result. Information technologies. – T. 11, № 1, 2026. – P. 29-39. DOI: 10.18413/2518-1092-2026-11-1-0-4
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