Results 131 to 140 of about 39,997 (249)

Quantifier based aggregation in LSP suitability map construction [PDF]

open access: yes, 2011
Bronselaer, Antoon   +3 more
core  

On the Limitations of Speaker Diarization

open access: yesExpert Systems, Volume 43, Issue 3, March 2026.
ABSTRACT Although speaker diarization has evolved to be more robust and more refined, including incorporating modern automatic speech recognition (ASR), current systems still suffer from several disruptive factors, like noise. We comprehensively evaluate the limitations of current diarization systems to uncover the underlying causes that hinder ...
Joana Amorim   +2 more
wiley   +1 more source

Navigating Narratives: An Exploratory Scoping Review on the Framing of the Illegal, Unreported and Unregulated Fishing Research

open access: yesFish and Fisheries, Volume 27, Issue 2, Page 179-195, March 2026.
ABSTRACT Sustainable fisheries are often undermined by illegal, unreported and, in some cases, unregulated fishing (collectively, IUU fishing). As such, it is critical to ensure that current research effectively informs practical fisheries management interventions.
Brittany Bartlett   +10 more
wiley   +1 more source

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature. [PDF]

open access: yesInt J Intell Syst, 2022
Alsalem MA   +12 more
europepmc   +1 more source

Addressing Bias in Spoken Language Systems Used in the Development and Implementation of Automated Child Language‐Based Assessment

open access: yesJournal of Educational Measurement, Volume 63, Issue 1, Spring 2026.
Abstract This article addresses bias in Spoken Language Systems (SLS) that involve both Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) and reports experiments to improve the performance of SLS for automated language and literacy‐related assessments with students who are under served in the U.S. educational system.
Alison L. Bailey   +5 more
wiley   +1 more source

AI and Measurement Concerns: Dealing with Imbalanced Data in Autoscoring

open access: yesJournal of Educational Measurement, Volume 63, Issue 1, Spring 2026.
Abstract Unbiasedness for proficiency estimates is important for autoscoring engines since the outcome might be used for future learning or placement. Imbalanced training data may lead to certain biases and lower the prediction accuracy for classification algorithms.
Yunting Liu   +3 more
wiley   +1 more source

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