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arXiv · 2411.13314

I2TTS: Image-indicated Immersive Text-to-speech Synthesis with Spatial Perception

Jiawei Zhang·Tian-Hao Zhang·Jun Wang·Jiaran Gao·Xinyuan Qian·Xu-Cheng Yin
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Abstract

Controlling the style and characteristics of speech synthesis is crucial for adapting the output to specific contexts and user requirements. Previous Text-to-speech (TTS) works have focused primarily on the technical aspects of producing natural-sounding speech, such as intonation, rhythm, and clarity. However, they overlook the fact that there is a growing emphasis on spatial perception of synthesized speech, which may provide immersive experience in gaming and virtual reality. To solve this issue, in this paper, we present a novel multi-modal TTS approach, namely Image-indicated Immersive Text-to-speech Synthesis (I2TTS). Specifically, we introduce a scene prompt encoder that integrates visual scene prompts directly into the synthesis pipeline to control the speech generation process. Additionally, we propose a reverberation classification and refinement technique that adjusts the synthesized mel-spectrogram to enhance the immersive experience, ensuring that the involved reverberation condition matches the scene accurately. Experimental results demonstrate that our model achieves high-quality scene and spatial matching without compromising speech naturalness, marking a significant advancement in the field of context-aware speech synthesis. Project demo page: https://spatialTTS.github.io/ Index Terms-Speech synthesis, scene prompt, spatial perception

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Jiawei Zhang, Tian-Hao Zhang, Jun Wang, Jiaran Gao, Xinyuan Qian, Xu-Cheng Yin. 2024-11-20. I2TTS: Image-indicated Immersive Text-to-speech Synthesis with Spatial Perception. https://arxiv.org/abs/2411.13314

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