most citedADD 2023: the Second Audio Deepfake Detection Challenge

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cs.SD2024

Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio

Xinrui Yan, Jiangyan Yi, Jianhua Tao +6

Open environment oriented open set model attribution of deepfake audio is an emerging research topic, aiming to identify the generation models of deepfake audio. Most previous work…

cs.SD2024

WMCodec: End-to-End Neural Speech Codec with Deep Watermarking for Authenticity Verification

Junzuo Zhou, Jiangyan Yi, Yong Ren +3

Recent advances in speech spoofing necessitate stronger verification mechanisms in neural speech codecs to ensure authenticity. Current methods embed numerical watermarks before co…

cs.SD2024

TraceableSpeech: Towards Proactively Traceable Text-to-Speech with Watermarking

Junzuo Zhou, Jiangyan Yi, Tao Wang +5

Various threats posed by the progress in text-to-speech (TTS) have prompted the need to reliably trace synthesized speech. However, contemporary approaches to this task involve add…

cs.SD2023

Fewer-token Neural Speech Codec with Time-invariant Codes

Yong Ren, Tao Wang, Jiangyan Yi +4

Language model based text-to-speech (TTS) models, like VALL-E, have gained attention for their outstanding in-context learning capability in zero-shot scenarios. Neural speech code…

cs.SD202320 cited

ADD 2023: the Second Audio Deepfake Detection Challenge

Jiangyan Yi, Jianhua Tao, Ruibo Fu +15

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the…