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Text-to-speech synthesis (TTS) has witnessed rapid progress in recent years,where neural methods became capable of producing audios with high naturalness.However, these efforts still suffer from two types of latencies: (a) the {\emcomputational latency} (synthesizing time), which grows linearly with thesentence length even with parallel approaches, and (b) the {\em input latency}in scenarios where the input text is incrementally generated (such as insimultaneous translation, dialog generation, and assistive technologies). Toreduce these latencies, we devise the first neural incremental TTS approachbased on the recently proposed prefix-to-prefix framework. We synthesize speechin an online fashion, playing a segment of audio while generating the next,resulting in an $O(1)$ rather than $O(n)$ latency.

Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework