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We present a multi-speaker Japanese audiobook text-to-speech (TTS) systemthat leverages multimodal context information of preceding acoustic context andbilateral textual context to improve the prosody of synthetic speech. Previouswork either uses unilateral or single-modality context, which does not fullyrepresent the context information. The proposed method uses an acoustic contextencoder and a textual context encoder to aggregate context information andfeeds it to the TTS model, which enables the model to predict context-dependentprosody. We conducted comprehensive objective and subjective evaluations on amulti-speaker Japanese audiobook dataset. Experimental results demonstrate thatthe proposed method significantly outperforms two previous works. Additionally,we present insights about the different choices of context - modalities,lateral information and length - for audiobook TTS that have never beendiscussed in the literature before.

Improving Speech Prosody of Audiobook Text-to-Speech Synthesis with Acoustic and Textual Contexts