The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences. Recently, transformers have gained prominence across various speech-related domains, including automatic speech recognition, speech synthesis, speech translation, speech para-linguistics, speech enhancement, spoken dialogue systems, and numerous multimodal applications. In this paper, we present a comprehensive survey that aims to bridge research studies from diverse subfields within speech technology. By consolidating findings from across the speech technology landscape, we provide a valuable resource for researchers interested in harnessing the power of transformers to advance the field. We identify the challenges encountered by transformers in speech processing while also offering insights into potential solutions to address these issues.

翻译：Transformer在自然语言处理领域的显著成功激发了语音处理研究界的兴趣，促使研究者探索其在语音序列中建模长程依赖关系的潜力。近年来，Transformer在自动语音识别、语音合成、语音翻译、语音副语言特征分析、语音增强、口语对话系统及多模态应用等众多语音相关领域愈发受到重视。本文通过整合语音技术各子领域的研究成果，呈现了一项旨在连接该领域不同分支研究的全面综述。通过汇聚语音技术领域的发现，本文为有志于利用Transformer推动该领域发展的研究人员提供了宝贵资源。同时，我们识别了Transformer在语音处理中面临的挑战，并针对这些问题提出了潜在的解决方案。