Time Series-based Spoof Speech Detection Using Long Short-term Memory and Bidirectional Long Short-term Memory

Detecting fake speech in voice-based authentication systems is crucial for reliability. Traditional methods often struggle because they cannot handle the complex patterns over time. study introduces an advanced approach using deep learning, specifically long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) models, tailored for identifying fake speech based on its temporal characteristics. We use speech signals with cepstral features such as mel-frequency cepstral coefficients (MFCC), constant Q cepstral coefficients, and open-source and music interpretation by large-space extraction to directly learn these patterns. Testing on the ASVspoof 2019 Logical Access dataset, we focus on metrics such as min-tDCF, equal error recall, precision, and F1-score. Our results show that LSTM BiLSTM models significantly enhance the reliability of spoof detection systems.