[Submitted on 16 Jul 2026 (v1), last revised 21 Oct 2026 (this version, v2)]
Abstract:Modern speech systems are evolving into ecosystems of autonomous speech agents that interact, negotiate, and trade assets such as embeddings, spectrograms, or raw data. In such markets, sellers cannot reveal assets for evaluation due to privacy constraints, and buyers cannot price what they cannot test. Thus, we introduce WaveCommitments, an end-to-end design that couples multivariate polynomial commitments with confidential asset valuation via Data Shapley inside a trusted execution environment. It enables selective openings and tamper-evident provenance to prove that a delivered asset matches the valuation while hiding everything else. Our extensive experiments across three standard datasets and various tasks with state-of-the-art encoders, including Whisper, reveal that we speed up the strongest baselines by up to 18.1x while reducing their proof sizes by up to 43.2x. We also analyze how a speaker’s value shifts across tasks and encoders, revealing agent-level market prices.
Submission history
From: Oguzhan Baser [view email]
[v1] Thu, 16 Jul 2026 03:46:22 UTC (2,426 KB)
[v2] Wed, 21 Oct 2026 05:12:42 UTC (3,421 KB)
