This paper describes an unconventional way to use wav2vec 2.0 representations for voice conversion (VC) purpose. Our experiment shows that aggregate of hidden representations from wav2vec 2.0 layers is more effective in VC than using last-layer representation only. In particular, the aggregate of all hidden representations is dependent on a mainly required characteristic (e.g. vocal or linguistic) by a speech task—but such results are consistent on different datasets for the same task. Based on these results, we propose Wav2vec-VC that uses wav2vec 2.0 hidden-layer representations as input to the disentanglement-based VC. Given a target (/source) utterance, Wav2vec-VC gets an aggregated hidden representation weighted in order to perform the speaker (/content)-related tasks, and feeds it to a speaker (/content) encoder whose output is combined at a decoder to synthesize a voice-converted utterance. Our evaluation shows that Wav2vec-VC outperforms SOTA VC models in terms of both voice similarity and speech intelligibility.

The use of smartphones as voice recorders has made it easy to record audios as proof of conversations, but sharing of such audio evidence incurs speech and voice privacy risks. However, protecting speech/voice privacy without losing audio authenticity is challenging. The conventional post-process redaction and voice conversion of audio recordings, which invalidate their original signatures, make the audio unverifiable and prone to tampering. In this paper, we present Overo, an audio recording/sharing solution that supports privacy processing without losing audio authenticity. Overo records a realtime audio stream in the standard AAC-encoded format and allows privacy post-processing prior to sharing of audios while keeping their original signatures valid (even after the post redaction and voice conversion), guaranteeing no tampering since the time of their recording. Therefore, users can post-process their recordings to desired levels of privacy on speech (what content to redact) and speakers (whose voice to disguise) at the time of audio release, and still prove their authenticity. Overo is readily implementable in today's commodity smartphones. Our prototype on iPhones/Android phones demonstrates the production of AAC-compliant, tamperproof, and self-authenticating audios with speech/voice privacy protected based on users' post-recording decisions.

With various IoT cameras today, sharing of their video evidences, while benefiting the public, threatens the privacy of individuals in the footage. However, protecting visual privacy without losing video authenticity is challenging. The conventional post-process blurring would open the door for posterior fabrication, whereas the realtime blurring results in poor quality, low-frame-rate videos due to the limited processing power of commodity cameras. This paper presents Pinto, a software-based solution for producing privacy-protected, forgery-proof, and high-frame-rate videos using low-end IoT cameras. Pinto records a realtime video stream at a fast rate and allows post-processing for privacy protection prior to sharing of videos while keeping their original, realtime signatures valid even after the post blurring, guaranteeing no content forgery since the time of their recording. Pinto is readily implementable in today's commodity cameras. Our prototype on three different embedded devices, each deployed in a specific application context---on-site, vehicular, and aerial surveillance---demonstrates the production of privacy-protected, forgery-proof videos with frame rates of 17--24 fps, comparable to those of HD videos.