Explainable AI for forensic speech authentication within cognitive and computational neuroscience

TL;DR

This paper introduces a deep learning model with explainable AI to detect fake speech, using features that highlight audio inconsistencies.

Contribution

A novel CNN-LSTM framework with XAI techniques for interpretable forensic speech authentication is proposed.

Findings

The model achieves high accuracy using LFCC features over MFCC and GFCC.

XAI methods reveal the model focuses on high-frequency and temporal artifacts.

The approach is validated on ASVspoof2019 LA and WaveFake datasets.

Abstract

The proliferation of deepfake technologies presents serious challenges for forensic speech authentication. We propose a deep learning framework combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to improve detection of manipulated audio. Leveraging the spectral feature extraction of CNNs and the temporal modeling of LSTMs, the model demonstrates superior accuracy and generalization across the ASVspoof2019 LA and WaveFake datasets. Linear Frequency Cepstral Coefficients (LFCCs) were employed as acoustic features and outperformed MFCC and GFCC representations. To enhance transparency and trustworthiness, explainable artificial intelligence (XAI) techniques, including Grad-CAM and SHAP, were applied, revealing that the model focuses on high-frequency artifacts and temporal inconsistencies. These interpretable analyses validate both the models design and the forensic relevance of LFCC features. The proposed approach thus provides a robust, interpretable, and XAI-driven solution for forensic authentic detection.

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