Towards General Auditory Intelligence (arxiv.org)

A new survey, "Towards General Auditory Intelligence," synthesizes recent work on bringing audio into the era of large language and foundation models to create more human-like machine listening and speaking. Rather than treating audio as a narrow signal-processing task, the paper frames it as a rich semantic and emotional modality that must be deeply integrated with LLMs to enable naturalistic understanding, expressive generation, and spoken interaction. The authors organize the landscape around four pillars—audio comprehension, audio generation, speech-based interaction, and audio-visual understanding—and argue that progress across these areas is essential for audio-native AGI. Technically, the survey highlights trends toward multimodal architectures that combine powerful audio encoders with LLM-style reasoning, large-scale pretraining on audio–text (and audio–visual) corpora, and generative audio models (e.g., neural vocoders and diffusion-based synthesis) that can produce expressive outputs. It emphasizes how cross-modal fusion and joint embedding/attention mechanisms improve situational awareness and semantic grounding, while pointing out persistent challenges: data scale and diversity, evaluation metrics for semantic and affective audio understanding, robustness, real-time constraints, and ethical/privacy concerns. The paper maps concrete research directions for building systems that perceive, reason about, and speak with the fluidity of human auditory intelligence.