Algorithmic Detection of AI-Generated and Spoofed Speech for Interpretable Voice Authentication

Recent advances in neural speech synthesis and voice conversion have increased the risk of logical-access spoofing attacks against voice-authentication systems. This study presents an interpretable, enrollment-conditioned framework that separates speaker-identity verification from subsequent speech-authenticity assessment. The first stage uses Emphasized Channel Attention, Propagation and Aggregation Time Delay Neural Network (ECAPA-TDNN) speaker embeddings and cosine similarity, while the second stage evaluates speaker-conditioned Mel-Frequency Cepstral Coefficient (MFCC), pitch, and local-jitter anomaly scores. These scores are normalized using development target bona fide distributions and combined through a fixed, unweighted score-level fusion; Principal Component Analysis (PCA) waveform reconstruction is retained as an ablation component. The framework was evaluated on the ASVspoof 2019 Logical Access (LA) benchmark using strict separation between development-based calibration and independent evaluation. All feature formulations, normalization references, fusion components, and decision thresholds were frozen before evaluation. On 102,579 evaluation trials, the ECAPA-TDNN stage alone achieved a target False Rejection Rate (FRR) of 0.5773%, a bona fide non-target False Acceptance Rate (FAR) of 1.0532%, and a spoof acceptance rate of 66.1892%. The complete hierarchical system achieved an observed target FRR of 4.8045%, a non-target FAR of 0.7681%, and a spoof acceptance rate of 58.7239%, corresponding to an absolute spoof-acceptance reduction of 7.4653 percentage points. MFCC provided the strongest standalone second-stage discrimination, with a Receiver Operating Characteristic–Area Under the Curve (ROC-AUC) of 0.653055, while the frozen MFCC-pitch-jitter fusion achieved an ROC-AUC of 0.604293 and was selected prospectively under a 5% development target-FRR constraint. Attack-specific evaluation revealed substantial variability across the A07–A19 evaluation conditions, including systematic failure modes for A12 and A16. Within the ASVspoof 2019 LA evaluation setting, the results demonstrate a reproducible and interpretable hierarchical authentication framework while also showing that fixed acoustic descriptors do not provide attack-invariant spoof detection. Because development and evaluation originate from the same benchmark, these results should not be interpreted as evidence of cross-corpus, cross-channel, physical-access, or real-world deployment generalization.