2 papers
eess.AS2026
Decaf: A privacy preserving speech codec using speaker disentanglement and canonical voice conversion
Md Shakhrul Iman Siam, Dushyant Sharma, Stanislav Yu. Kruchinin +1
We present DECAF, a privacy preserving neural speech codec that obfuscates a speaker's voice while preserving linguistic content while maintaining automatic speech recognition (ASR…
eess.AS2022
End-to-end speech recognition modeling from de-identified data
Martin Flechl, Shou-Chun Yin, Junho Park +1
De-identification of data used for automatic speech recognition modeling is a critical component in protecting privacy, especially in the medical domain. However, simply removing a…