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Related Experiment Video

Updated: Jul 3, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs.

Emad S Hassan^1,2, Badawi Neyazi³, H S Seddeq⁴

¹Department of Electrical Engineering, College of Engineering, Jazan University, Jizan, Saudi Arabia.

|February 14, 2024

Summary

This summary is machine-generated.

This study enhances speaker identification in noisy environments using Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms. Discrete Wavelet Transform (DWT) excelled in standard identification, while Discrete Cosine Transform (DCT) performed best in cancelable systems.

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Area of Science:

Speech processing and signal analysis
Biometric security systems
Machine learning for pattern recognition

Background:

Speaker identification systems face challenges in noisy and reverberant acoustic environments, impacting accuracy.
Existing methods struggle with robustness and security against potential intruders.
Cancelable biometrics aims to enhance security by creating non-reversible transformations of biometric features.

Purpose of the Study:

To enhance speaker identification system efficacy in adverse acoustic conditions.
To develop a novel cancelable speaker identification system robust to reverberation.
To evaluate the performance of various feature extraction techniques in different system configurations.

Main Methods:

Feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT).
Modeling reverberation using comb filters and analyzing pitch frequency via Auto Correlation Function (ACF).
Implementing a cancelable framework involving comb filtering for feature distortion to obscure speaker identity.

Main Results:

Discrete Wavelet Transform (DWT)-based features showed superior performance in the reverberation-affected speaker identification system.
Discrete Cosine Transform (DCT)-based features achieved the highest performance in the cancelable speaker identification system.
The proposed novel system demonstrated resilience in both reverberant and open environments.

Conclusions:

Feature selection is crucial for optimizing speaker identification performance based on system requirements (standard vs. cancelable) and environmental conditions.
The proposed comb filtering technique effectively creates cancelable features for enhanced biometric security.
The study provides effective solutions for robust speaker identification in challenging real-world acoustic scenarios.