自然性
计算机科学
语音识别
说话人识别
身份(音乐)
人工智能
代表(政治)
语音合成
质量(理念)
机器学习
模式识别(心理学)
哲学
物理
认识论
量子力学
政治
声学
政治学
法学
标识
DOI:10.1142/s0218001423500155
摘要
Deepfake technology, especially deep voice, which has been derived from artificial intelligence in recent years, is potentially harmful, and the public is not yet wary. However, many speech synthesis models measure the degree of true restitution by Mean Opinion Rating (MOS), a subjective assessment of naturalness and quality of speech by human subjects, but in future it will be difficult to distinguish the interlocutor’s identity through the screen. For this reason, this study addresses the threat posed by this new technology by combining representational learning and 0transfer learning in two sub-systems: a recognition system and a voice print system. The recognition system is responsible for the detection of which voice is a fake voice generated by speech conversion or speech synthesis techniques, while the acoustic system is responsible for the verification of the speaker’s identity through acoustic features. In the speech recognition system, we use the representation learning method and the transfer classification method. We use X-vector data for training, and then fine-tune the model using four types of marker data to learn the representation vectors of real and fake voice, and use support vector machine to classify real and fake voice in the back-end to reduce the negative effect of the new technique.
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