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arXiv · 2605.26672

Can We Hear from Events? Generating Speech from Event Camera

Jingping Fang·Lin Chen·Chenyang Xu·Tong Zhao·Weidong Cai·Xiaoming Chen
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Abstract

Traditional RGB-based speech generation faces Temporal Granularity Mismatch since fixed camera exposure times inevitably blur the high-frequency articulatory transients essential for rendering emotional speech. To break this ceiling, we propose EventSpeech as a novel text-conditioned framework pioneering the use of neuromorphic events for expressive speech generation, since these microsecond-precise events naturally align with acoustic waveform dynamics. Our architecture integrates a dedicated Event Encoder to model sparse neuromorphic events alongside a multi-scale Audio Encoder featuring a Hierarchical Wavelet Contextualizer (HWC). A bidirectional alignment mechanism seamlessly synchronizes linguistic content and visual dynamics with dense acoustic features. Furthermore, we construct EVT-SPK as the first benchmark comprising large-scale synthetic data and real-world recordings from specialized neuromorphic hardware. Extensive evaluations demonstrate that EventSpeech significantly outperforms current baselines by preserving fine-grained emotions and resisting motion blur to establish a new paradigm for multimodal speech generation. Code and demo are available at https://xrfang-0102.github.io/EventSpeechWeb/.

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Jingping Fang, Lin Chen, Chenyang Xu, Tong Zhao, Weidong Cai, Xiaoming Chen. 2026-05-26. Can We Hear from Events? Generating Speech from Event Camera. https://arxiv.org/abs/2605.26672

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