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When Large Language Models Meet Speech: A Survey on Integration Approaches
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Recent advancements in large language models (LLMs) have spurred interest in expanding their application beyond text-based tasks. A large number of studies have explored integrating other modalities with LLMs, notably speech modality, which is naturally related to text. This paper surveys the integration of speech with LLMs, categorizing the methodologies into three primary approaches: text-based, latent-representation-based, and audio-token-based integration. We also demonstrate how these methods are applied across various speech-related applications and highlight the challenges in this field to offer inspiration for
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Breaking the Barriers of Text-Hungry and Audio-Deficient AI
A proposed audio-native translation framework called MAST with fractional diffusion is described, but no evidence is given that it produces working translations.
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