Predicting Parkinson's Disease (PD) progression is crucial, and voice biomarkers offer a non-invasive method for tracking symptom severity (UPDRS scores) through telemonitoring. Analyzing this longitudinal data is challenging due to within-subject correlations and complex, nonlinear patient-specific progression patterns. This study benchmarks LMMs against two advanced hybrid approaches: the Generalized Neural Network Mixed Model (GNMM) (Mandel 2021), which embeds a neural network within a GLMM structure, and the Neural Mixed Effects (NME) model (Wortwein 2023), allowing nonlinear subject-specific parameters throughout the network. Using the Oxford Parkinson's telemonitoring voice dataset, we evaluate these models' performance in predicting Total UPDRS to offer practical guidance for PD research and clinical applications.

I was recently waiting for my nails to dry and didn't want to smudge the paint, when it dawned on me that this would be the perfect opportunity to test some voice-only artificial intelligence features. Silicon Valley car owners are having long conversations with ChatGPT as they drive around, and I wanted to try chatting hands-free before meeting with two OpenAI product leads later that day. Even though chatbots can be helpful for brainstorms, speaking back-and-forth with ChatGPT was like collaborating with an over-caffeinated friend who can't stand even a second of silence. I was valiantly fighting against the artificial intelligence tool to finish a single, complete thought before it cut me off. Me: I wrote a newsletter called AI Unlocked last year for our readers.

OpenAI is bringing audio and image capabilities to ChatGPT. The platform, which has long been limited to written prompts, will be adding the new features over the next two weeks to paid versions of the app, OpenAI announced in a blog post on Monday. Everyone else will be receiving the features "soon after". Users can have voice conversations with the chatbot, bringing it closer to popular AI assistants such as Apple's Siri and Amazon's Alexa. ChatGPT's new voice feature can also narrate bedtime stories, settle debates at the dinner table and speak out loud text input from users.

OpenAI's ChatGPT is getting a major update that will enable the viral chatbot to have voice conversations with users and interact using images, moving it closer to popular artificial intelligence (AI) assistants like Apple's Siri. The voice feature "opens doors to many creative and accessibility-focused applications", OpenAI said in a blog post on Monday. Similar AI services like Siri, Google voice assistant and Amazon's Alexa are integrated with the devices they run on and are often used to set alarms and reminders, and deliver information off the internet. Since its debut last year, ChatGPT has been adopted by companies for a wide range of tasks from summarizing documents to writing computer code, setting off a race among big tech companies to launch their own offerings based on generative AI. Google has imminent plans to launch its answer to ChatGPT, called Gemini, which is reportedly already being tested by a small group of companies.

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. Experiment results show that the PSGT has better performance in both prediction error and stability over compared methods.

Stress during public speaking is common and adversely affects performance and self-confidence. Extensive research has been carried out to develop various models to recognize emotional states. However, minimal research has been conducted to detect stress during public speaking in real time using voice analysis. In this context, the current review showed that the application of algorithms was not properly explored and helped identify the main obstacles in creating a suitable testing environment while accounting for current complexities and limitations. In this paper, we present our main idea and propose a stress detection computational algorithmic model that could be integrated into a Virtual Reality (VR) application to create an intelligent virtual audience for improving public speaking skills. The developed model, when integrated with VR, will be able to detect excessive stress in real time by analysing voice features correlated to physiological parameters indicative of stress and help users gradually control excessive stress and improve public speaking performance

Detection of common events and scenes from audio is useful for extracting and understanding human contexts in daily life. Prior studies have shown that leveraging knowledge from a relevant domain is beneficial for a target acoustic event detection (AED) process. Inspired by the observation that many human-centered acoustic events in daily life involve voice elements, this paper investigates the potential of transferring high-level voice representations extracted from a public speaker dataset to enrich an AED pipeline. Towards this end, we develop a dual-branch neural network architecture for the joint learning of voice and acoustic features during an AED process and conduct thorough empirical studies to examine the performance on the public AudioSet [1] with different types of inputs. Our main observations are that: 1) Joint learning of audio and voice inputs improves the AED performance (mean average precision) for both a CNN baseline (0.292 vs 0.134 mAP) and a TALNet [2] baseline (0.361 vs 0.351 mAP); 2) Augmenting the extra voice features is critical to maximize the model performance with dual inputs.

For many, the worst-case privacy scenario involves corporations and governments that listen to what we say in the privacy of our own homes. Unfortunately, that dystopian future may not be as distant as you thought. When is Google Voice listening to what you say and how can you stop it? The key is in your Google Voice Search history. Google Voice Search (commonly known as Google Voice, although this is technically the name of Google's unrelated telephony service) allows users to perform Google searches, set reminders or alarms, and perform other functions using only their voices. Every single request a user makes is stored on their account, and those recordings can be reviewed (and listened to) by the user at any time.