A Blog post by Technology Innovation Institute on Hugging Face

Background: Scalable remote approaches are increasingly needed to identify cognitive impairment in clinical research. The Everyday Cognition (ECog) scale and its short form (12-item Everyday Cognition [ECog12]) capture subjective cognitive and functional decline, whereas automated speech-based story recall provides an objective measure of cognitive performance. Whether speech recall contributes information beyond remote questionnaire assessment remains unclear. Objective: This study aimed to determine whether automated speech-based story recall measures provide incremental discrimination of cognitive impairment diagnoses beyond the self-reported ECog12 (Self-ECog12) scale and demographics, both without and with 2 additional subjective memory measures that assess current memory/thinking concerns and perceived recent decline. Methods: This cross-sectional study included 7268 individuals from the Alzheimer’s Disease Neuroimaging Initiative 4 (ADNI4) Remote Digital cohort with complete baseline questionnaire, demographic, and Storyteller speech-recall data. The outcome was defined as a participant report of a prior diagnosis of mild cognitive impairment or memory problem, Alzheimer disease, or dementia (n=574, 7.9%). Participants completed a smartphone-based Storyteller assessment involving immediate and delayed spoken recall of standardized stories; recordings were automatically analyzed to derive 5 prespecified recall and language measures. Participants were stratified into training and independent natural-prevalence test cohorts. Logistic regression models evaluated Self-ECog12 alone; Self-ECog12 plus demographics; Storyteller added to that model; the 2 additional subjective memory measures; and the full model incorporating Storyteller. Model development and threshold selection were confined to training data. Discrimination, calibration, and threshold-dependent performance were evaluated in the held-out test cohort. Area under the receiver operating characteristic curve (AUC) comparisons used paired DeLong tests. Results: The test cohort included 1817 participants, of whom 143 (7.9%) reported a prior diagnosis. Self-ECog12 alone achieved an AUC of 0.792 (95% CI 0.755-0.828), increasing to 0.827 (95% CI 0.792-0.859) with demographics. Adding Storyteller increased AUC to 0.853 (95% CI 0.821-0.883; ΔAUC 0.026, 95% CI 0.009‐0.043; paired DeLong =.002). With the 2 subjective memory measures included, AUC was 0.857 (95% CI 0.830-0.882) and increased to 0.875 (95% CI 0.851-0.899) after adding Storyteller (ΔAUC 0.019, 95% CI 0.008‐0.030;

There’s an older deaf gentleman in my town who’s very friendly, and likes to communicate with rudimentary hand gestures, but I have a hard time understand what he’s trying to say. I’d love to learn Am...

A Backdoor That Listens for One Voice Researchers have taught speech recognition systems to recognize a specific person by voice. At that moment, the system secretly changes the person's words. In the most dangerous attack scenario, the model quietly added a request to transfer $5,000 to the transcript. A connected AI agent then made the payment in a test banking environment.

There’s an older deaf gentleman in my town who’s very friendly, and likes to communicate with rudimentary hand gestures, but I have a hard time understand what he’s trying to say. I’d love to learn American Sign Language (ASL) at some point (shoutout King Ron), but until then, I wanted an app that could convert speech into text. Every transcription app I found required a pretty expensive subscription… so I decided to build my own using web technologies!

Learn how speech recognition software works with tables and how testing with AT can identify small changes that make it better for everyone.

MBZUAIとInception42が発表した最大375BのJais 3モデル群と音声認識を解説。