Human subjective evaluation plays a crucial role in evaluating speech-related generative tasks such as text-to-speech (TTS) generation. However, current practices are often constrained by limited scalability, fragmented workflows, and inconsistent rating reliability. Researchers frequently rely on manual methods or general-purpose crowdsourcing systems, where recruiting appropriately skilled listeners is challenging, and result analysis is labor-intensive. In this work, we introduce Achoio, a dedicated end-to-end online system designed to streamline and scale human evaluation for the TTS research community. Achoio allows researchers to create and manage evaluation projects, upload synthesized speech samples, and automatically match them with qualified listeners based on linguistic proficiency and domain knowledge. The system provides built-in tools for project status tracking, result aggregation and visualization. In this demonstration, we will walk through the core features of Achoio, including intuitive project setup, skill-based listener matching algorithm, and automated analytics. By addressing the limitations of existing workflows, Achoio offers a scalable, domain-aware, and analysis-ready solution for conducting high-quality subjective TTS evaluations. Our system is live and can be found at https://www.achoio.com. Demo is available on YouTube at https://youtu.be/Ugjj3_YooSM.

Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users’ privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this article, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable Internet of Things (IoT) devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data, minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, and weight. Our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared with similar studies. 1 Furthermore, our approach is novel compared with existing studies, as it does not require collecting and using users’ sensitive information. Thus, it preserves the users’ privacy.

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.