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SafeEar is an innovative audio depth detection framework that is capable of detecting depth audio without relying on speech content. This framework protects the privacy of speech content by designing a neural audio codec that separates semantic and acoustic information from audio samples and only uses acoustic information (such as prosody and timbre) for deep detection. SafeEar improves the detector's capabilities by enhancing the codec in the real world, allowing it to recognize a wide range of deep audio. Extensive experiments on the framework on four benchmark datasets show that SafeEar is highly effective in detecting various deep techniques, with equal error rates (EER) as low as 2.02%. At the same time, it also protects speech content in five languages from being deciphered by machine and human auditory analysis, as demonstrated by our user research and word error rate (WER) above 93.93%. In addition, SafeEar also builds a benchmark for anti-depth and anti-content recovery evaluation, providing a basis for future research in the field of audio privacy protection and depth detection.
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