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Related Experiment Video

Updated: May 7, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
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Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Speech emotional features extraction based on electroglottograph.

Lijiang Chen1, Xia Mao, Pengfei Wei

  • 1School of Electronic and Information Engineering, Beihang University, Beijing 100191, China clj@ee.buaa.edu.cn.

Neural Computation
|September 20, 2013
PubMed
Summary
This summary is machine-generated.

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This study introduces novel speech emotional features from electroglottography (EGG) and speech signals. These new features outperform traditional methods for speaker- and content-independent emotion recognition.

Area of Science:

  • Speech processing
  • Biomedical engineering
  • Machine learning for emotion recognition

Background:

  • Accurate speech emotion recognition is crucial for human-computer interaction.
  • Existing methods often struggle with speaker and content independence.
  • Electroglottography (EGG) provides valuable vocal fold excitation data.

Purpose of the Study:

  • To propose novel speech emotional features using EGG and speech signals.
  • To evaluate the effectiveness of these features for emotion recognition.
  • To compare proposed features against traditional methods.

Main Methods:

  • Extraction of power-law distribution coefficients (PLDC) from voiced segment durations (vocal fold excitation).
  • Calculation of discrete cosine transform coefficients from normalized spectra (vocal tract modulation).

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Last Updated: May 7, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
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Published on: August 9, 2024

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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  • Feature selection using sequential forward/backward floating search and emotion recognition via support vector machine (SVM).

    • Main Results:

    • Proposed features effectively capture vocal fold and vocal tract modulation information.
    • Features demonstrate superior performance in speaker-independent emotion recognition.
    • Features show improved results in content-independent emotion recognition tasks.

    Conclusions:

    • The proposed EGG and speech signal-based features offer enhanced performance for emotion recognition.
    • These features provide a more robust approach compared to traditional methods.
    • The findings contribute to more accurate and generalizable speech emotion recognition systems.