This is the paper that made zero-shot voice cloning practical: train a speaker encoder on a discriminative verification task with thousands of noisy, transcript-free voices, then feed those embeddings into a Tacotron 2 synthesizer and a WaveNet vocoder. The result is a TTS system that can mimic a new speaker from a few seconds of reference audio, even speakers it never saw during training.

This long review of the Jia et al. work from Google walks through all three modules in detail, the speaker encoder trained on a verification objective, the sequence-to-sequence mel-spectrogram synthesizer conditioned on the embedding, and the autoregressive WaveNet vocoder. It also unpacks the ablations that quantify just how much speaker diversity you need in the encoder to generalize, plus the surprising result that randomly sampled embeddings produce coherent novel voices. If you're building anything in the voice cloning, personalized TTS, or speaker embedding space, this paper is required reading and the deep-dive walks through every piece.