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Implementing machine learning techniques for continuous emotion prediction from uniformly segmented voice recordings

Psychology

Implementing machine learning techniques for continuous emotion prediction from uniformly segmented voice recordings

H. Diemerling, L. Stresemann, et al.

Discover a groundbreaking method for predicting emotions from short audio samples! Researchers Hannes Diemerling, Leonie Stresemann, Tina Braun, and Timo von Oertzen have leveraged advanced machine learning techniques to achieve accuracy that rivals human evaluative benchmarks. Dive into the world of real-time emotion detection!

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Playback language: English
Abstract
This study introduces a novel method for continuous emotion prediction from short (1.5 s) audio samples, using machine learning techniques. Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and a hybrid C-DNN model were trained on German and English audio data from Emo-DB and RAVDESS databases. Results show accuracy significantly exceeding random guessing, comparable to human evaluative benchmarks, suggesting potential for real-time emotion detection.
Publisher
Frontiers in Psychology
Published On
Authors
Hannes Diemerling, Leonie Stresemann, Tina Braun, Timo von Oertzen
Tags
emotion prediction
machine learning
audio analysis
Deep Neural Networks
real-time detection

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