Brain-to-speech technology represents a fusion of interdisciplinary
applications encompassing fields of artificial intelligence, brain-computer
interfaces, and speech synthesis. Neural representation learning based
intention decoding and speech synthesis directly connects the neural activity
to the means of human linguistic communication, which may greatly enhance the
naturalness of communication. With the current discoveries on representation
learning and the development of the speech synthesis technologies, direct
translation of brain signals into speech has shown great promise. Especially,
the processed input features and neural speech embeddings which are given to
the neural network play a significant role in the overall performance when
using deep generative models for speech generation from brain signals. In this
paper, we introduce the current brain-to-speech technology with the possibility
of speech synthesis from brain signals, which may ultimately facilitate
innovation in non-verbal communication. Also, we perform comprehensive analysis
on the neural features and neural speech embeddings underlying the
neurophysiological activation while performing speech, which may play a
significant role in the speech synthesis works.

脑到语音技术可直接将脑信号转化为语音，通过神经表征学习与语音合成相结合，以提高自然交流的流畅性，可能为非语言沟通带来创新。本文介绍了当前的脑到语音技术，探讨了脑信号转化为语音的可能性，并对执行语音时的神经特征和神经语音嵌入进行了综合分析，这对于语音合成的工作具有重要作用。