A dimensional approach to emotion recognition of speech from movies

Θεόδωρος Γιαννακόπουλος · Aggelos Pikrakis · Sergios Theodoridis

66 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition Speech and Audio Processing Music and Audio Processing

Abstract

In this paper we present a novel method for extracting affective information from movies, based on speech data. The method is based on a 2D representation of speech emotions (Emotion Wheel). The goal is twofold. First, to investigate whether the Emotion Wheel offers a good representation for emotions associated with speech signals. To this end, several humans have manually annotated speech data from movies using the Emotion Wheel and the level of disagreement has been computed as a measure of representation quality. The results indicate that the emotion wheel is a good representation of emotions in speech data. Second, a regression approach is adopted, in order to predict the location of an unknown speech segment in the Emotion Wheel. Each speech segment is represented by a vector of ten audio features. The results indicate that the resulting architecture can estimate emotion states of speech from movies, with sufficient accuracy.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 66 citations exist upstream.

References

2001Emotion recognition in human-computer interaction 2003Speech emotion recognition using hidden Markov models 2001Speech emotion recognition using hidden Markov models 2006Extracting moods from pictures and sounds: towards truly personalized TV 2008Recognizing Human Emotional State From Audiovisual Signals

Cited by

2012Speech-based recognition of self-reported and observed emotion in a dimensional space 2019Improving multilingual speech emotion recognition by combining acoustic features in a three-layer model 2018Predicting Arousal and Valence from Waveforms and Spectrograms Using Deep Neural Networks 2020Nonnegative Matrix Factorization Based Transfer Subspace Learning for Cross-Corpus Speech Emotion Recognition 2020Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM 2018Emotion Recognition from Speech 2021Efficient Feature-Aware Hybrid Model of Deep Learning Architectures for Speech Emotion Recognition 2014Review on Speech Emotion Recognition

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.