[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82617-en":3,"doc-seo-82617-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82617,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score","Room embeddings derived from reverberant speech can be unreliable because speech content and recording degradation alter the representation even when speaker and geometry are fixed, reducing downstream performance. A new framework learns robust room embeddings and a representation-level uncertainty score from reverberant speech without downstream-task supervision. Embeddings are anchored to a structured RIR latent space, trained with multi-view KL alignment and multi-positive contrastive learning, then calibrated via dispersion-driven, rank-based learning for single-utterance selective prediction.","Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score  \nYang Xiang  1, Philipp Go¨tz2, Emanue¨l A. P. Habets  2, Andreas Walther3, Wenwu Wang 1,  \nPhilip J. B. Jackson  1  \n1 Centre for Vision Speech and Signal Processing, University of Surrey, United Kingdom  \n2 International Audio Laboratories Erlangen∗ , Erlangen, Germany  \n3 Fraunhofer Institute for Integrated Circuits IIS, Erlangen, Germany  \n[yang.xiang@surrey.ac.uk](yang.xiang@surrey.ac.uk) , [p.jackson@surrey.ac.uk](p.jackson@surrey.ac.uk)  \narXiv :2607 .0 1527v 1 [ cs . SD] 1 Jul 2026  \nAbstract  \nRoom 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 embeddingsand optimized with a rank-based objective. Across waveformand spectrogram-level corruptions, the score is consistent with representation dispersion and enables effective selective prediction while requiring only a single utterance at inference. Index Terms: room acoustics, reverberant speech, representation learning, contrastive learning, uncertainty estimation  \n1. Introduction  \nReverberant speech carries cues about the acoustic environment, enabling applications such as blind estimation of physical parameters (e.g., reverberation time T60 and clarity index C50 ) [1–3], acoustic-environment retrieval/verification [4] and robust speech processing [5] . Existing approaches broadly fall into two directions: (i) task-specific blind inference, including room impulse response (RIR) reconstruction [6–8] and acoustic-parameter estimation, and (ii) task-agnostic representation learning that yields reusable room embeddings [9–11], which can be transferred across downstream objectives.  \nHowever, learning reliable room embeddings remains challenging because the observed signal is confounded by non-room factors (e.g., content, speaker, noise) . In practice, recordings are rarely clean: noise and partial signal loss (e.g., dropouts, bandwidth limits) can substantially alter embeddings even when the room and source–receiver geometry are unchanged, degrading representation quality and downstream robustness. This motivates a general-purpose uncertainty score that indicates whether an embedding should be trusted. While uncertainty estimation has been widely studied in classification and regression [12, 13], recent blind room-acoustic inference work has begun to quantify uncertainty by calibrating error bounds  \n∗ A joint institution of Fraunhofer IIS and Friedrich-AlexanderUniversitt Erlangen-N¨urnberg (FAU), Germany.  \nfor RIR/parameter estimates derived from learned environment representations [11] . Yet, predicting task-agnostic uncertainty scores for representation reliability with controlled speech corruptions is underexplored.  \nFollowing [11], we learn a task-agnostic room representation from reverberant speech together with an uncertainty score reflecting representation reliability under corruption. Our approach has three stages. First, we learn a structured roomacoustic latent space using a variational autoencoder (VAE) trained on log-mel RIR spectrograms. Second, we train a speech encoder with multi-view training and contrastive learning to improve robustness to speech-content variation while remaining aligned with the RIR latent space. Third, we train a lightweight uncer","cbCaigZ8vu5gFKlE","https://ap.wps.com/l/cbCaigZ8vu5gFKlE","pdf",746514,1,6,"English","en",105,"# Abstract\n# Introduction\n# Problem Formulation\n# Training Pipeline Overview\n# Method Stages\n## Stage-1: RIR-VAE Latent Space\n## Stage-2: Speech Embedding Robustness\n## Stage-3: Dispersion-Calibrated Uncertainty Score\n# Evaluation and Reliability Assessment","[{\"question\":\"Why are blindly estimated room embeddings from reverberant speech often unreliable?\",\"answer\":\"They change when speech content and recording degradation are present, even if speaker, room, and geometry remain unchanged. This confounds the representation and can degrade downstream task performance.\"},{\"question\":\"How does the proposed method learn robust room embeddings without downstream-task supervision?\",\"answer\":\"It anchors embeddings to a structured RIR latent space, uses multi-view KL-based alignment with a multi-positive contrastive term, and trains representations to be robust to speech-content variation.\"},{\"question\":\"What does the dispersion-calibrated uncertainty score measure and how is it used?\",\"answer\":\"It measures representation reliability under controlled corruptions by mapping corruption-induced embedding dispersion to a single-utterance uncertainty estimate. It enables selective prediction while requiring only one utterance at inference.\"}]",1784181839,15,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"quantifying-the-uncertainty-of-blindly-estimated-room-embeddings-using-a-dispersion-calibrated-score","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/quantifying-the-uncertainty-of-blindly-estimated-room-embeddings-using-a-dispersion-calibrated-score/82617/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are blindly estimated room embeddings from reverberant speech often unreliable?","Question",{"text":75,"@type":76},"They change when speech content and recording degradation are present, even if speaker, room, and geometry remain unchanged. This confounds the representation and can degrade downstream task performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method learn robust room embeddings without downstream-task supervision?",{"text":80,"@type":76},"It anchors embeddings to a structured RIR latent space, uses multi-view KL-based alignment with a multi-positive contrastive term, and trains representations to be robust to speech-content variation.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the dispersion-calibrated uncertainty score measure and how is it used?",{"text":84,"@type":76},"It measures representation reliability under controlled corruptions by mapping corruption-induced embedding dispersion to a single-utterance uncertainty estimate. 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