Results 41 to 50 of about 719,897 (244)
Global–Local Self-Attention Based Transformer for Speaker Verification
Applied Sciences, 2022 Transformer models are now widely used for speech processing tasks due to their powerful sequence modeling capabilities. Previous work determined an efficient way to model speaker embeddings using the Transformer model by combining transformers with ...
Fei Xie, Dalong Zhang, Chengming Liu
doaj +1 more source
Liver organoids: modelling complexity in homeostasis and disease
FEBS Letters, EarlyView.Studying liver in vitro has been challenging because simple 2D cell cultures fail to capture liver's cellular and architectural complexity. To bridge this gap, scientists increasingly use organoids, 3D liver models which better mimic liver composition and function. This review examines recent advances in liver organoid complexity and realism, discusses
Anna M. Dowbaj, Meritxell Huch
wiley +1 more source
Speaker verification using I-vector features [PDF]
, 2014 This PhD research has provided novel solutions to three major challenges which have prevented the wide spread deployment of speaker recognition technology: (1) combating enrolment/ verification mismatch, (2) reducing the large amount of development and ...
Kanagasundaram, Ahilan
core
Applied Sciences, 2021 The majority of recent speaker verification tasks are studied under open-set evaluation scenarios considering real-world conditions. The characteristics of these tasks imply that the generalization towards unseen speakers is a critical capability.
Ju-Ho Kim +3 more
doaj +1 more source
Hijacking emergency granulopoiesis: Neutrophil ontogeny and reprogramming in cancer
Molecular Oncology, EarlyView.Neutrophils are highly plastic innate immune cells; their functions in cancer extend beyond the tumour microenvironment. This Review summarises current understanding of neutrophil maturation and heterogeneity and highlights tumour‐induced granulopoiesis as a systemic programme that expands immature, immunosuppressive neutrophils via tumour‐derived ...
Gabriela Marinescu, Yi Feng
wiley +1 more source
Speaker Model Clustering to Construct Background Models for Speaker Verification
Archives of Acoustics, 2017 Conventional speaker recognition systems use the Universal Background Model (UBM) as an imposter for all speakers. In this paper, speaker models are clustered to obtain better imposter model represen- tations for speaker verification purpose.
Gökay DİŞKEN +2 more
doaj +1 more source
Loss of IGF‐1R impairs DNA‐PKcs recruitment to chromatin leading to defective end‐joining
Molecular Oncology, EarlyView.IGF‐1R promotes radioresistance by facilitating DNA‐PKcs recruitment to chromatin, enabling non‐homologous end‐joining (NHEJ) repair of double‐strand breaks. Inhibition or loss of IGF‐1R disrupts this recruitment to damage sites, driving compensatory reliance on microhomology‐mediated end‐joining (MMEJ) repair.
Matthew O. Ellis +3 more
wiley +1 more source
Speaker verification incorporating high-level linguistic features [PDF]
, 2008 Speaker verification is the process of verifying or disputing the claimed identity of a speaker based on a recorded sample of their speech. Automatic speaker verification technology can be applied to a variety of person authentication and identification ...
Baker, Brendan J.
core
Dictionary Attacks on Speaker Verification
IEEE Transactions on Information Forensics and Security, 2023 In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models.
Mirko Marras +3 more
openaire +4 more sources
Loss of proton‐sensing TDAG8 increases tumor progression in mouse models of colon cancer
Molecular Oncology, EarlyView.Loss of the pH‐sensing receptor TDAG8 accelerates colorectal cancer progression in mice. Animals lacking TDAG8 expression had increased tumor growth, DNA damage, and recruitment of tumor‐associated immune cells, including macrophages, neutrophils, and monocytes.
Ermanno Malagola +11 more
wiley +1 more source