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This paper describes Mixer-TTS, a non-autoregressive model formel-spectrogram generation. The model is based on the MLP-Mixer architectureadapted for speech synthesis. The basic Mixer-TTS contains pitch and durationpredictors, with the latter being trained with an unsupervised TTS alignmentframework. Alongside the basic model, we propose the extended version whichadditionally uses token embeddings from a pre-trained language model. BasicMixer-TTS and its extended version achieve a mean opinion score (MOS) of 4.05and 4.11, respectively, compared to a MOS of 4.27 of original LJSpeech samples.Both versions have a small number of parameters and enable much faster speechsynthesis compared to the models with similar quality.

Mixer-TTS: non-autoregressive, fast and compact text-to-speech model conditioned on language model embeddings