{"ID":5676028,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-04T22:43:54.027453447Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01527","arxiv_id":"2607.01527","title":"Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score","abstract":"Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when speaker, room, and source-receiver geometry remain unchanged, degrading downstream task performance. We propose a framework that learns room embeddings robust to speech-content variation and a representation-level uncertainty score from reverberant speech without downstream-task supervision. The embedding is anchored to a structured room impulse response (RIR) latent space and trained using a multi-view data structure with Kullback-Leibler (KL)-based alignment; a multi-positive contrastive term further refines robustness. A lightweight uncertainty head is calibrated using the dispersion of corruption-induced embeddings and optimized with a rank-based objective. Across waveform- and spectrogram-level corruptions, the score is consistent with representation dispersion and enables effective selective prediction while requiring only a single utterance at inference.","short_abstract":"Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when speaker, room, and source-receiver geometry remain unchanged, degrading downstream task performance. We propose a framework that learns room embeddings robust to speech-c...","url_abs":"https://arxiv.org/abs/2607.01527","url_pdf":"https://arxiv.org/pdf/2607.01527v1","authors":"[\"Yang Xiang\",\"Philipp Götz\",\"Emanuël A. P. Habets\",\"Andreas Walther\",\"Wenwu Wang\",\"Philip J. B. Jackson\"]","published":"2026-07-01T22:55:49Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.LG\"]","methods":"[]","has_code":false}