Enhancing Voice Spoofing Detection: A Hybrid Approach With VGGish-LSTM Model for Improved Security in Automatic Speaker Verification Systems

Cybercrimes frequently manifest through the utilization of the internet. Spoofing attacks epitomize an advanced facet of contemporary cybercrime. Considerable research has been conducted in the domain of these diverse spoofing attack techniques. Recent spoofing attacks have predominantly concentrate on voice spoofing, driven by the vulnerabilities of Automatic Speaker Verification systems. Such attacks contains significant threats, resulting in fraudulent activities including imitation of someone’s voice for financial scam, bypassing security locks, or getting sensitive information and data. To address these concerns, this research focuses on the detection of voice spoofing attacks by utilizing a hybrid VGGish-LSTM model. While previous transfer learning methodologies have essentially been applied to image classification tasks, our methodology focuses on voice classification. In comparison to VGGish, another transfer learning model i.e. YAMNet is also utilized. These models support the extraction of significant features i.e. embeddings, capable of mitigating bias and facilitating analysis with limited datasets. For classification, LSTM and 1D CNN models are applied on the extracted embeddings. The evaluation uncovers that the hybrid VGGish-LSTM model exhibits better performance than alternative configurations. We achieved a remarkable accuracy of 99.11% with an equal error rate of 1.18% on ASV Spoof 2019 LA dataset. This research helps the general public to recognize spoof attacks, thereby helping in the prevention of frauds by the detection of synthesized speech.