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The mapping of text to speech (TTS) is non-deterministic, letters may bepronounced differently based on context, or phonemes can vary depending onvarious physiological and stylistic factors like gender, age, accent, emotions,etc. Neural speaker embeddings, trained to identify or verify speakers aretypically used to represent and transfer such characteristics from referencespeech to synthesized speech. Speech separation on the other hand is thechallenging task of separating individual speakers from an overlapping mixedsignal of various speakers. Speaker attractors are high-dimensional embeddingvectors that pull the time-frequency bins of each speaker's speech towardsthemselves while repelling those belonging to other speakers. In this work, weexplore the possibility of using these powerful speaker attractors forzero-shot speaker adaptation in multi-speaker TTS synthesis and propose speakerattractor text to speech (SATTS). Through various experiments, we show thatSATTS can synthesize natural speech from text from an unseen target speaker'sreference signal which might have less than ideal recording conditions, i.e.reverberations or mixed with other speakers.

SATTS: Speaker Attractor Text to Speech, Learning to Speak by Learning to Separate