Indonesian Speech Anti-Spoofing System: Data Creation and Convolutional Neural Network Models
Sarah Azka Arief, Candy Olivia Mawalim, Dessi Puji Lestari · 2024
Biometric systems are prone to spoofing attacks. While research in speech anti-spoofing has been progressing, there is a limited availability of diverse language datasets. This study aims to bridge this gap by developing an Indonesian spoofed speech dataset, which includes replay attacks, text-to-speech, and voice conversion. This dataset forms the foundation for creating an Indonesian speech anti-spoofing system. Subsequently, light convolutional neural network (LCNN) and residual network (ResNet) models, based on convolutional neural networks (CNN), were developed to evaluate the dataset. The input features used are linear frequency cepstral coefficients (LFCC). Both models demonstrate remarkably low minDCF and EER scores approaching zero. The results also exhibit exceptional scores with 4-fold cross validation, showing strong initial performance with no signs of overfitting. However, models trained solely on Common Voice or Prosa.ai datasets performed poorly in cross-source tests, suggesting generalization issues due to a lack of diversity in the dataset. This highlights the need for further improvement and continued research in Indonesian speech spoof detection.