preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202409.2449.v1 (registering DOI)

Preprint Article Version 1 This version is not peer-reviewed

Version 1 : Received: 29 September 2024 / Approved: 30 September 2024 / Online: 1 October 2024 (08:43:37 CEST)

The modulation of vocal elements such as pitch, loudness, and duration plays a crucial role in conveying both linguistic information and the speaker’s emotional state. While acoustic features like fundamental frequency (F0) variability have been widely studied in emotional speech analysis, challenges remain in accurately classifying emotions due to the complex and dynamic nature of vocal expressions. Traditional analytical methods often oversimplify these dynamics, potentially overlooking intricate patterns indicative of specific emotions. This study aims to enhance emotion classification in speech by directly incorporating dynamic F0 contours into the analytical framework using Generalized Additive Mixed Models (GAMMs). We utilized the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), focusing on eight distinct emotional states expressed by 24 professional actors. Sonorant segments were extracted, and F0 measurements were converted into semitones relative to a 100 Hz baseline to standardize pitch variations. By employing GAMMs, we modeled non-linear trajectories of F0 contours over time, accounting for both fixed effects (emotions) and random effects (individual speaker variability). Our analysis revealed that incorporating emotion-specific non-linear time effects and individual speaker differences significantly improved the model’s explanatory power, ultimately explaining up to 66.5% of the variance in F0. The inclusion of random smooths for time within speakers captured individual temporal modulation patterns, providing a more accurate representation of emotional speech dynamics. The results demonstrate that dynamic modeling of F0 contours using GAMMs enhances the accuracy of emotion classification in speech. This approach captures the nuanced pitch patterns associated with different emotions and accounts for individual variability among speakers. The findings contribute to a deeper understanding of the vocal expression of emotions and offer valuable insights for advancing speech emotion recognition systems.

emotional speech recognition; fundamental frequency (F0); pitch contours; generalized additive mixed models (GAMMs); non-linear dynamics; speech processing

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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