针对语音合成竞赛，构建能识别多种合成语音的鲁棒分类模型。
Synthetic Speech Classification: IEEE Signal Processing Cup 2022 challenge
- 用多种文本转语音算法生成数据训练深度学习模型
- 直接使用原始音频数据的深度模型效果最佳
- 适合语音安全、伪造检测方向的研究者参考
本项目旨在为IEEE信号处理杯2022挑战赛实现并设计一个稳健的合成语音分类器。我们利用来自多种文本转语音（TTS）算法以及未知TTS算法生成的语音数据，训练合成语音归属模型。实验对比了支持向量机、高斯混合模型等经典机器学习方法，以及基于ResNet、VGG16和两种浅层端到端网络的深度学习方法。结果表明，采用原始数据的深度学习方法表现最优。
原文摘要 · Abstract (English)
The aim of this project is to implement and design arobust synthetic speech classifier for the IEEE Signal ProcessingCup 2022 challenge. Here, we learn a synthetic speech attributionmodel using the speech generated from various text-to-speech(TTS) algorithms as well as unknown TTS algorithms. Weexperiment with both the classical machine learning methodssuch as support vector machine, Gaussian mixture model, anddeep learning based methods such as ResNet, VGG16, and twoshallow end-to-end networks. We observe that deep learningbased methods with raw data demonstrate the best performance.
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