  Brain-inspired Spiking Neural Network (SNN) has demonstrated its
effectiveness and efficiency in vision, natural language, and speech
understanding tasks, indicating their capacity to "see", "listen", and "read".
In this paper, we design \textbf{SpikeVoice}, which performs high-quality
Text-To-Speech (TTS) via SNN, to explore the potential of SNN to "speak". A
major obstacle to using SNN for such generative tasks lies in the demand for
models to grasp long-term dependencies. The serial nature of spiking neurons,
however, leads to the invisibility of information at future spiking time steps,
limiting SNN models to capture sequence dependencies solely within the same
time step. We term this phenomenon "partial-time dependency". To address this
issue, we introduce Spiking Temporal-Sequential Attention STSA in the
SpikeVoice. To the best of our knowledge, SpikeVoice is the first TTS work in
the SNN field. We perform experiments using four well-established datasets that
cover both Chinese and English languages, encompassing scenarios with both
single-speaker and multi-speaker configurations. The results demonstrate that
SpikeVoice can achieve results comparable to Artificial Neural Networks (ANN)
with only 10.5 energy consumption of ANN.
