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While modern Text-to-Speech (TTS) systems can produce natural-soundingspeech, they remain unable to reproduce the full diversity found in naturalspeech data. We consider the distribution of all possible real speech samplesthat could be generated by these speakers alongside the distribution of allsynthetic samples that could be generated for the same set of speakers, using aparticular TTS system. We set out to quantify the distance between real andsynthetic speech via a range of utterance-level statistics related toproperties of the speaker, speech prosody and acoustic environment. Differencesin the distribution of these statistics are evaluated using the Wassersteindistance. We reduce these distances by providing ground-truth values atgeneration time, and quantify the improvements to the overall distributiondistance, approximated using an automatic speech recognition system. Our bestsystem achieves a 10\% reduction in distribution distance.

Evaluating and reducing the distance between synthetic and real speech distributions