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Revisiting Speech Content Privacy
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In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportunity to revisit speech content protection. There are many different applications of content privacy, even though this area has been under-explored in speech technology research. This paper presents several scenarios that indicate a need for speech content privacy even as the specific techniques to achieve content privacy may necessarily vary. Our discussion includes several different types of content privacy including recoverable and non-recoverable content. Finally, we introduce evaluation strategies as well as describe some of the difficulties that may be encountered.
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Cited by 1 Pith paper
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SecureSpeech: Prompt-based Speaker and Content Protection
A dual anonymization pipeline that uses an LLM to replace sensitive entities and a prompt-driven TTS to generate speech with a new, unrelated voice.
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