INDONESIAN SPEECH ANTI-SPOOFING SYSTEM: DATA CREATION AND CONVOLUTIONAL NEURAL NETWORK MODELS
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...
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| Main Author: | |
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/85050 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | 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. |
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