A Comparison of CQT Spectrogram with STFT-based Acoustic Features in Deep Learning-based Synthetic Speech Detection | ||
| Journal of AI and Data Mining | ||
| دوره 11، شماره 1، فروردین 2023، صفحه 119-129 اصل مقاله (1.74 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2022.12373.2382 | ||
| نویسندگان | ||
| P. Abdzadeh؛ H. Veisi* | ||
| Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran. | ||
| چکیده | ||
| Automatic Speaker Verification (ASV) systems have proven to be vulnerable to various types of presentation attacks, among which Logical Access attacks are manufactured using voice conversion and text-to-speech methods. In recent years, there has been loads of work concentrating on synthetic speech detection, and with the arrival of deep learning-based methods and their success in various computer science fields, they have been a prevailing tool for this very task too. Most of the deep neural network-based techniques for synthetic speech detection have employed the acoustic features based on Short-Term Fourier Transform (STFT), which are extracted from the raw audio signal. However, lately, it has been discovered that the usage of Constant Q Transform's (CQT) spectrogram can be a beneficial asset both for performance improvement and processing power and time reduction of a deep learning-based synthetic speech detection. In this work, we compare the usage of the CQT spectrogram and some most utilized STFT-based acoustic features. As lateral objectives, we consider improving the model's performance as much as we can using methods such as self-attention and one-class learning. Also, short-duration synthetic speech detection has been one of the lateral goals too. Finally, we see that the CQT spectrogram-based model not only outperforms the STFT-based acoustic feature extraction methods but also reduces the processing time and resources for detecting genuine speech from fake. Also, the CQT spectrogram-based model places well among the best works done on the LA subset of the ASVspoof 2019 dataset, especially in terms of Equal Error Rate. | ||
| کلیدواژهها | ||
| Voice Spoofing Detection؛ Deep Neural Networks؛ Voice Biometrics؛ Deepfake Audio Detection؛ synthetic speech detection | ||
| مراجع | ||
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