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In this paper, we propose to unify the two aspects of voice synthesis, namelytext-to-speech (TTS) and vocoder, into one framework based on a pair of forwardand reverse-time linear stochastic differential equations (SDE). The solutionsof this SDE pair are two stochastic processes, one of which turns thedistribution of mel spectrogram (or wave), that we want to generate, into asimple and tractable distribution. The other is the generation procedure thatturns this tractable simple signal into the target mel spectrogram (or wave).The model that generates mel spectrogram is called It\^oTTS, and the model thatgenerates wave is called It\^oWave. It\^oTTS and It\^oWave use the Wienerprocess as a driver to gradually subtract the excess signal from the noisesignal to generate realistic corresponding meaningful mel spectrogram and audiorespectively, under the conditional inputs of original text or mel spectrogram.The results of the experiment show that the mean opinion scores (MOS) ofIt\^oTTS and It\^oWave can exceed the current state-of-the-art methods, andreached 3.925$\pm$0.160 and 4.35$\pm$0.115 respectively. The generated audiosamples are available at https://wushoule.github.io/ItoAudio/. All authorscontribute equally to this work.

It\^oTTS and It\^oWave: Linear Stochastic Differential Equation Is All You Need For Audio Generation