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Currently, many multi-speaker speech synthesis and voice conversion systemsaddress speaker variations with an embedding vector. Modeling it directlyallows new voices outside of training data to be synthesized. GMM basedapproaches such as Tacospawn are favored in literature for this generationtask, but there are still some limitations when difficult conditionings areinvolved. In this paper, we propose VoiceLens, a semi-supervised flow-basedapproach, to model speaker embedding distributions for multi-conditionalspeaker generation. VoiceLens maps speaker embeddings into a combination ofindependent attributes and residual information. It allows new voicesassociated with certain attributes to be \textit{generated} for existing TTSmodels, and attributes of known voices to be meaningfully \textit{edited}. Weshow in this paper, VoiceLens displays an unconditional generation capacitythat is similar to Tacospawn while obtaining higher controllability andflexibility when used in a conditional manner. In addition, we showsynthesizing less noisy speech from known noisy speakers without re-trainingthe TTS model is possible via solely editing their embeddings with a SNRconditioned VoiceLens model. Demos are available atsos1sos2sixteen.github.io/voicelens.

VoiceLens: Controllable Speaker Generation and Editing with Flow