Minimum dietary diversity behaviour among children aged 6 to 24 months and their determinants: insights from 31 Sub-Saharan African (SSA) countries.

Background: Feeding practices during infancy have a significant impact on a child's cognitive development and long-term health outcomes. Dietary diversity guidelines from the WHO and UNICEF recommend a diverse range of foods for children aged below 24 months for their optimal growth and development. However, in sub-Saharan Africa (SSA), little is known about the extent to which dietary diversity behaviour in children aged 6 to 24 months aligns with the recommendations and the factors associated with the differentials in dietary behaviour.

Enhancing dysarthric speech recognition through SepFormer and hierarchical attention network models with multistage transfer learning.

Dysarthria, a motor speech disorder that impacts articulation and speech clarity, presents significant challenges for Automatic Speech Recognition (ASR) systems. This study proposes a groundbreaking approach to enhance the accuracy of Dysarthric Speech Recognition (DSR). A primary innovation lies in the integration of the SepFormer-Speech Enhancement Generative Adversarial Network (S-SEGAN), an advanced generative adversarial network tailored for Dysarthric Speech Enhancement (DSE), as a front-end processing stage for DSR systems.

Tunable synthesis of magnetoelectric CoFeO-BaTiO core-shell nanowires.

A template-assisted synthesis approach was employed to tune the structure and properties of CoFeO-BaTiO core-shell magnetoelectric nanowires. By adjusting the composition of the nanowires, we achieved control over the magnetic anisotropy in the CoFeO core phase. This work highlights the potential for enhanced magnetic anisotropy to improve magnetoelectric performance.

A blended framework for audio spoof detection with sequential models and bags of auditory bites.

An automated speaker verification system uses the process of speech recognition to verify the identity of a user and block illicit access. Logical access attacks are efforts to obtain access to a system by tampering with its algorithms or data, or by circumventing security mechanisms. DeepFake attacks are a form of logical access threats that employs artificial intelligence to produce highly realistic audio clips of human voice, that may be used to circumvent vocal authentication systems.