Digital voice recordings contain valuable information that can indicate an individual’s cognitive health, offering a non-invasive and efficient method for assessment. Research has demonstrated that digital voice measures can detect early signs of cognitive decline by analyzing features such as speech rate, articulation, pitch variation and pauses, which may signal cognitive impairment when deviating from normative patterns.

However, voice data introduces privacy challenges due to the personally identifiable information embedded in recordings, such as gender, accent and emotional state, as well as more subtle speech characteristics that can uniquely identify individuals. These risks are amplified when voice data is processed by automated systems, raising concerns about re-identification and potential misuse of data.

In a new study, researchers from Boston University Chobanian & Avedisian School of Medicine have introduced a computational framework that applies pitch-shifting, a sound recording technique that changes the pitch of a sound, either raising or lowering it, to protect speaker 

identity while preserving acoustic features essential for cognitive assessment.

People with favourable socioeconomic conditions, such as high incomes or education levels, face a reduced risk of age-related diseases and show fewer signs of biological ageing than peers of the same age, finds a new study led by University College London (UCL) researchers.

Antimicrobial resistance has recently increased due to emerging carbapenem-resistant Klebsiella pneumoniae and extended-spectrum β-lactamase (ESBL)-producing strains of K. pneumoniae. Osaka Metropolitan University researchers conducted a systematic review and meta-analysis to evaluate the prevalence of ESBL-producing and carbapenem-resistant strains in hmKp and non-hmKp clinical isolates.