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The recent text-to-speech (TTS) has achieved quality comparable to that ofhumans; however, its application in spoken dialogue has not been widelystudied. This study aims to realize a TTS that closely resembles humandialogue. First, we record and transcribe actual spontaneous dialogues. Then,the proposed dialogue TTS is trained in two stages: first stage, variationalautoencoder (VAE)-VITS or Gaussian mixture variational autoencoder (GMVAE)-VITSis trained, which introduces an utterance-level latent variable intovariational inference with adversarial learning for end-to-end text-to-speech(VITS), a recently proposed end-to-end TTS model. A style encoder that extractsa latent speaking style representation from speech is trained jointly with TTS.In the second stage, a style predictor is trained to predict the speaking styleto be synthesized from dialogue history. During inference, by passing thespeaking style representation predicted by the style predictor toVAE/GMVAE-VITS, speech can be synthesized in a style appropriate to the contextof the dialogue. Subjective evaluation results demonstrate that the proposedmethod outperforms the original VITS in terms of dialogue-level naturalness.

End-to-End Text-to-Speech Based on Latent Representation of Speaking Styles Using Spontaneous Dialogue