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A Semi-Supervised Speech Deception Detection Algorithm Combining Acoustic Statistical Features and Time-Frequency

Hongliang Fu^1,2, Hang Yu¹, Xuemei Wang^1,2

¹Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.

|May 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel semi-supervised speech deception detection algorithm. The new method enhances accuracy by combining acoustic statistical and time-frequency features, improving lie detection capabilities.

Keywords:

consistency regularization deception detection feature fusion hybrid network semi-supervised

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Area of Science:

Cognitive Neuroscience
Speech Processing
Machine Learning

Background:

Human deception involves complex cognitive neural mechanisms.
Speech deception detection models often suffer from poor generalization due to inappropriate feature selection.
Advanced feature extraction is crucial for improving the accuracy of semi-supervised deception detection.

Purpose of the Study:

To propose a novel semi-supervised speech deception detection algorithm.
To enhance the generalization ability of deception detection models.
To improve the accuracy of detecting deception in speech.

Main Methods:

A hybrid semi-supervised neural network combining an autoencoder network (AE) and a mean-teacher network was developed.
Static artificial statistical features were processed by the AE for robust advanced feature extraction.
Three-dimensional (3D) mel-spectrum features were processed by the mean-teacher network for time-frequency information.
Consistency regularization was applied post-feature fusion to mitigate overfitting.

Main Results:

The proposed algorithm achieved a highest recognition accuracy of 68.62% on a self-built corpus.
This represents a 1.2% improvement in accuracy compared to the baseline system.
The method effectively enhanced the generalization ability and detection accuracy.

Conclusions:

The hybrid semi-supervised approach effectively extracts robust features for speech deception detection.
Combining acoustic statistical and time-frequency features improves model generalization and accuracy.
The proposed algorithm offers a promising advancement in semi-supervised speech deception detection.