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Massive Sound Embedding Benchmark (MSEB)

Georg Heigold, Ehsan Variani, Tom Bagby, Cyril Allauzen, Ji Ma, Shankar Kumar, Michael Riley

TL;DR

The paper introduces Massive Sound Embedding Benchmark (MSEB) to standardize evaluation of auditory components in multimodal systems through an embedding-centric lens. It defines eight diverse super tasks and a four-dataset suite, anchored by the novel SVQ corpus, and provides a model-agnostic evaluator and a modular, open-source library that supports bulk encoder inference. Initial experiments reveal substantial headroom between current audio-based approaches and text-based/oracle baselines, with pronounced cross-language and noise-condition variability, underscoring the need for universal, robust sound representations. The work invites community collaboration and releases the library publicly to accelerate the development of robust machine auditory intelligence.

Abstract

Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, each task involves transforming a raw audio signal into a meaningful 'embedding' - be it a single vector, a sequence of continuous or discrete representations, or another structured form - which then serves as the basis for generating the task's final response. To accelerate progress towards robust machine auditory intelligence, we present the Massive Sound Embedding Benchmark (MSEB): an extensible framework designed to evaluate the auditory components of any multimodal system. In its first release, MSEB offers a comprehensive suite of eight core tasks, with more planned for the future, supported by diverse datasets, including the new, large-scale Simple Voice Questions (SVQ) dataset. Our initial experiments establish clear performance headrooms, highlighting the significant opportunity to improve real-world multimodal experiences where audio is a core signal. We encourage the research community to use MSEB to assess their algorithms and contribute to its growth. The library is publicly hosted at github.

Massive Sound Embedding Benchmark (MSEB)

TL;DR

The paper introduces Massive Sound Embedding Benchmark (MSEB) to standardize evaluation of auditory components in multimodal systems through an embedding-centric lens. It defines eight diverse super tasks and a four-dataset suite, anchored by the novel SVQ corpus, and provides a model-agnostic evaluator and a modular, open-source library that supports bulk encoder inference. Initial experiments reveal substantial headroom between current audio-based approaches and text-based/oracle baselines, with pronounced cross-language and noise-condition variability, underscoring the need for universal, robust sound representations. The work invites community collaboration and releases the library publicly to accelerate the development of robust machine auditory intelligence.

Abstract

Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, each task involves transforming a raw audio signal into a meaningful 'embedding' - be it a single vector, a sequence of continuous or discrete representations, or another structured form - which then serves as the basis for generating the task's final response. To accelerate progress towards robust machine auditory intelligence, we present the Massive Sound Embedding Benchmark (MSEB): an extensible framework designed to evaluate the auditory components of any multimodal system. In its first release, MSEB offers a comprehensive suite of eight core tasks, with more planned for the future, supported by diverse datasets, including the new, large-scale Simple Voice Questions (SVQ) dataset. Our initial experiments establish clear performance headrooms, highlighting the significant opportunity to improve real-world multimodal experiences where audio is a core signal. We encourage the research community to use MSEB to assess their algorithms and contribute to its growth. The library is publicly hosted at github.
Paper Structure (17 sections, 18 figures, 9 tables)

This paper contains 17 sections, 18 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Massive Sound Embedding Benchmark (MSEB)
  3. Datasets
  4. Tasks
  5. Evaluators
  6. Library design
  7. Experiments
  8. Conclusion
  9. SVQ Dataset
  10. Retrieval
  11. Reranking
  12. Reasoning
  13. Classification
  14. Transcription
  15. Segmentation
  16. ...and 2 more sections

Figures (18)

  • Figure 1: The eight MSEB super tasks, spanning Information Access (Retrieval, Reasoning, Reranking), Core Perception (Transcription, Classification, Segmentation), and Organization & Generation (Clustering, Reconstruction).
  • Figure 2: The MSEB library architecture, illustrating the flow from Task Dataset to Leaderboard via bulk inference and evaluation.
  • Figure 3: Performance overview of the eight MSEB super tasks. Green bars show performance when sound was used as input to the task, while blue bars show performance using the corresponding ground-truth text transcript. Range markers indicate variability across locales or domains.
  • Figure 4: Typical page length distributions (in Gemini Embedding tokens).
  • Figure 5: Whisper word error rates (left) and sentence/query error rates (right) across different languages and environments.
  • ...and 13 more figures