Towards General Auditory Intelligence: Large Multimodal Models for Machine Listening and Speaking
Abstract
In the era of large language models (LLMs) and artificial general intelligence (AGI), computer audition must evolve beyond traditional paradigms to fully leverage the capabilities of foundation models, towards more comprehensive understanding, more natural generation and more human-like interaction. Audio, as a modality rich in semantic, emotional, and contextual cues, plays a vital role in achieving naturalistic and embodied machine intelligence. This survey provides a comprehensive review of recent progress in integrating audio into LLMs, with a focus on four key areas: audio comprehension, audio generation, speech-based interaction, and audio-visual understanding. We analyze how LLMs are reshaping audio perception and reasoning, enabling systems to understand sound at a deeper semantic level, generate expressive audio outputs, and engage in human-like spoken interaction. Furthermore, we explore how the fusion of audio and visual modalities enhances situational awareness and cross-modal reasoning, pushing the boundaries of multimodal intelligence. This survey not only synthesizes existing research but also identifies critical challenges and future directions for building audio-native AGI systems capable of perceiving, understanding, and interacting through sound as naturally as humans do.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is its comprehensive review of the integration of audio into large multimodal models, synthesizing existing research and identifying future challenges in the pursuit of general auditory intelligence. The survey provides valuable insights into the evolving landscape of machine listening and speaking, although it lacks original experimental contributions that would enhance its technical impact.
Methodology
The paper provides a thorough survey of the integration of audio into large language models (LLMs), addressing four main areas: audio comprehension, audio generation, speech-based interaction, and audio-visual understanding. The methodology is primarily qualitative, synthesizing existing research rather than presenting new experimental results. The authors effectively categorize and analyze the advancements in these areas, highlighting how LLMs reshape audio perception and reasoning. However, the lack of original experimental work limits the methodological rigor typically expected in a research paper.
Results & evaluation
As a survey paper, it does not present new experiments or datasets. Instead, it reviews existing literature and identifies trends and gaps in current research. While this approach is valuable for synthesizing knowledge, it does not provide empirical validation of the discussed concepts. The paper could have benefited from case studies or examples of implementations to illustrate the discussed methodologies.
Reproducibility
The paper lacks specific implementation details or code repositories that would allow for reproducibility of the findings. As it primarily reviews existing work, it does not present new methodologies that could be reproduced. The absence of a project URL or demo further limits the ability to verify the claims made in the survey.
Limitations
The paper's primary limitation is its survey nature, which does not contribute new experimental findings or methodologies. Additionally, the broad scope may dilute the depth of analysis in specific areas. The authors identify challenges and future directions, but without empirical data, the proposed future work remains speculative.
Broader impact
The integration of audio into LLMs has significant implications for the development of more naturalistic and human-like AI systems. The paper highlights the potential for advancements in machine listening and speaking, which could enhance applications in various fields, including human-computer interaction, assistive technologies, and entertainment. The exploration of audio-visual understanding also suggests avenues for improving situational awareness in AI systems. The main contribution of this paper is its comprehensive review of the integration of audio into large multimodal models, synthesizing existing research and identifying future challenges in the pursuit of general auditory intelligence. The survey provides valuable insights into the evolving landscape of machine listening and speaking, although it lacks original experimental contributions that would enhance its technical impact.
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