Abstract
This paper presents the Interspeech 2026 Audio Encoder Capability Challenge, a benchmark specifically designed to evaluate and advance the performance of pre-trained audio encoders as front-end modules for Large Audio Language Models (LALMs). While LALMs have shown remarkable understanding of complex acoustic scenes, their performance depends on the semantic richness of the underlying audio encoder representations. This challenge addresses the integration gap by providing a unified generative evaluation framework, XARES-LLM, which assesses submitted encoders across a diverse suite of downstream classification and generation tasks. By decoupling encoder development from LLM fine-tuning, the challenge establishes a standardized protocol for general-purpose audio representations that can effectively be used for the next generation of multimodal language models.
Abstract (translated)
本文介绍了Interspeech 2026音频编码器能力挑战赛，这是一项专门为评估和提升预训练音频编码器作为大型音频语言模型前端模块的性能而设计的基准测试。尽管大型音频语言模型已展现出对复杂声学场景的卓越理解能力，但其性能仍取决于底层音频编码器表征的语义丰富度。本挑战赛通过提供统一的生成式评估框架XARES-LLM，解决了这一集成难题——该框架通过多样化的下游分类与生成任务对提交的编码器进行综合评估。通过将编码器开发与大型语言模型微调解耦，本挑战赛为通用音频表征建立了标准化协议，这些表征可有效应用于下一代多模态语言模型。
URL
https://arxiv.org/abs/2603.22728
