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Azure Speaker Recognition is designed to recognize and verify individual speakers based on their unique voice characteristics. It's primarily used for authentication and security purposes, where the system can identify if the voice belongs to the claimed individual. The technology is based on deep learning models that analyze various features of the speaker's voice to make an identification or verification.

However, Azure Speaker Recognition, like other speaker recognition systems, is intended to differentiate between individuals based on their voice biometrics, not necessarily to determine if a voice is human or generated by AI (synthetic). While advanced speaker recognition systems can sometimes detect certain characteristics that might indicate a voice is synthetic, especially if the AI-generated voice has noticeable flaws, this is not their primary function.

For detecting AI-generated voices specifically or preventing identity impersonation through voice, there are other approaches and technologies that might be more directly focused on distinguishing between human and synthetic voices. This field is evolving rapidly, as AI-generated voices become more realistic, prompting ongoing research into detection methods. Some of these methods include analyzing the spectral, temporal, and prosodic features of the voice that might not be perfectly replicated by AI systems.

In summary, while Azure Speaker Recognition might provide some level of differentiation between human and AI voices indirectly through its biometric analysis, its primary purpose is to recognize and verify individual human speakers. For detecting impersonation or distinguishing between human and AI voices explicitly, specialized solutions or additional layers of analysis would likely be necessary.