Listeners often hear cloned voices as different speakers, study finds

When asked whether a real recording and a voice clone belong to the same person, listeners said 'different' 41% of the time, even though the speaker labels are identical.

Academic
Xudong Tang · Matthew Groh

Northwestern University

Research Digest··3 min read

In one line

Listeners judge a voice clone and a real recording of the same speaker to be different people in 41% of judgments, and no speech model matches human perception.

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An idea this paper builds on
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Voice security systems treat a voice as proof of who someone is, checking recordings against the speaker's identity label. But people do not always hear things that way, and modern synthetic speech can copy a voice so closely that listeners cannot tell it is fake.

To measure this, listeners were played pairs of recordings and asked whether both were the same person. The pairs mixed real recordings, synthetic copies (voice clones, made by a text-to-speech system that generates speech from text), and blends of two speakers' voices. Eleven speech models were then scored on how well their similarity judgments matched the listeners' answers.

Listener judgments went against the identity label most often for clones, where 41 percent of responses called a clone and a real recording of the same speaker different people. No model reached the level of agreement that listeners show with each other, so current systems do not yet capture how people actually hear identity.

How it works

stage by stage
COMPARE MODELS WITH LISTENER JUDGMENTSListen to eachpairCollectjudgmentsScore each pairListener…PredictedsimilarityVoice pairs: Real recordings, clones, morphsVoice pairsReal recordings,clones, morphsListeners judge pairs: Same speaker or different speakersListeners judgepairsSame speaker ordifferent speakersVIPBench: 124,876 listener judgmentsVIPBench124,876 listenerjudgmentsSpeech models: Similarity scores for voice pairsSpeech modelsSimilarity scores for voicepairsAgreement with listeners: Model scores versus humanAgreement withlistenersModel scores versus human
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Research Digest

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