VoxMem: Benchmarking Multimodal Memory in Large Audio Language Models
Spoken conversational systems must recover information from prior interactions (i.e., memory), yet relevant information in speech extends beyond what was said to who said it, how it was spoken, and what was audible, information that exists only in the audio signal and cannot be recovered from a transcript. Beyond what to remember, memory also demands diverse operations: retriev
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Spoken conversational systems must recover information from prior interactions (i.e., memory), yet relevant information in speech extends beyond what was said to who said it, how it was spoken, and what was audible, information that exists only in the audio signal and cannot be recovered from a transcript. Beyond what to remember, memory also demands diverse operations: retriev
This work highlights the critical need for advanced memory capabilities in spoken conversational AI, moving beyond text-centric approaches. For ML engineers, understanding how to design and evaluate models that can process and retain complex multimodal information across sessions is becoming increasingly important.