cs.SD · 2026-06-29 · No. 38
Sound, 2026-06-29.
5 new papers in cs.SD. Titles, authors,
abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →
01 — The papers
5 entries-
01
DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions
Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka, Emma Meisingseth, Karishma Jaitly
cs.SD · cs.AI · cs.CL · eess.AS
Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text, or images, repeated speaker identity across calls remains underused as an investigative signal. This paper presents DG^VoiC, a voice clustering framework for customer verification and cross-profile speaker linking on...
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02
From General-Purpose Audio Tagging to Spatially Grounded Sound Event Localization and Detection
Stefano Giacomelli, Stefano Damiano, Claudia Rinaldi, Fabio Graziosi, Toon van Waterschoot
cs.SD · cs.AI
This report investigates the extension of pretrained General-Purpose Audio Tagging (GP-AT) models toward spatially grounded Sound Event Localization and Detection (SELD). The proposed AT2SELD framework couples a pretrained AT backbone with compact First-Order Ambisonics (FOA) spatial processing, track-wise SED and Cartesian DOA estimation, permutation aware supervision, and calibration. It characterizes how semantic audio priors support...
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03
Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks
Andrew C. Cullen, Neil Marchant, Jiani Xie, Paul Montague, Benjamin I. P. Rubinstein
cs.SD · cs.AI · cs.CR · cs.LG
While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This is, in part, a product of the difficulties in scaling strictly digital adversarial workflows to the physical world. These scale barriers have led the community to abstract away key acoustic factors relating to detectability and the influence of geometry on acoustics. These methodological and...
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04
Advancing Speaker-Based Vocal Effort Classification with WavLM and Data Augmentation in Naturalistic Non-Calibrated Speech Recordings
Zahra Omidi, John H. L. Hansen
cs.SD · cs.LG
The variations in vocal effort range (e.g. whisper, soft, neutral, loud, shout) alter production and speech acoustics, reducing intelligibility and limiting the robustness of any subsequent speech technology. Classification is challenging since effort lies on a continuum, adjacent categories are easily confused, and labeled data remain scarce. Prior SSL approaches with wav2vec2, HuBERT, and AST improve performance on the AVID corpus but still...
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05
Learning from Annotation Uncertainty: Entropy-Aware Curriculum for Speech Emotion Recognition
Zahra Omidi, John H. L. Hansen
cs.SD · cs.LG
Speech emotion recognition (SER) often relies on hard consensus labels that collapse annotator disagreement. We study distribution-based supervision for 9-class SER on MSP-Podcast 2.0 using a WavLM-Base multitask model for categorical emotion and dimensional VAD. Hard-label training is compared with targets from primary and merged primary--secondary annotator vote distributions. Distributional objectives improve alignment with human vote...
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