| Sumario: | Introduction: The Toolkit to Examine Lifelike Language (TELL) is a web-based application providing speech biomarkers of neurodegeneration. After deployment of TELL v.1.0 in over 20 sites, we now introduce TELL v.2.0. Methods: First, we describe the app's usability features, including functions for collecting and processing data onsite, offline, and via videoconference. Second, we summarize its clinical survey, tapping on relevant habits (e.g., smoking, sleep) alongside linguistic predictors of performance (language history, use, proficiency, and difficulties). Third, we detail TELL's speech-based assessments, each combining strategic tasks and features capturing diagnostically relevant domains (motor function, semantic memory, episodic memory, and emotional processing). Fourth, we specify the app's new data analysis, visualization, and download options. Finally, we list core challenges and opportunities for development. Results: Overall, TELL v.2.0 offers scalable, objective, and multidimensional insights for the field. Conclusion: Through its technical and scientific breakthroughs, this tool can enhance disease detection, phenotyping, and monitoring. Plain Language Summary: Neurodegenerative disorders (NDs), such as Alzheimer's and Parkinson's disease, are a leading cause of disability, caregiver stress, and financial strain worldwide. The number of cases, now estimated at 60 million, will triple by 2050. Early detection is crucial to improve treatments, management, and financial planning. Unfortunately, standard diagnostic methods are costly, stressful, and often hard to access due to scheduling delays and availability issues. A promising alternative consists in digital speech analysis. This affordable, noninvasive approach can identify NDs based on individuals' voice recordings and their transcriptions. In 2023, we launched the Toolkit to Examine Lifelike Language (TELL), an online app providing robust speech biomarkers for clinical and research purposes. This paper introduces TELL v.2.0, a novel version with improved data collection, encryption, processing, storing, download, and visualization features. First, we explain the app's basic operations and its possibilities for online and offline data collection. Second, we describe its language survey, which covers questions about demographics as well as language history, usage, competence, and difficulties. Third, we describe TELL's speech tests, which assess key clinical features. Fourth, we outline the app's functions for analyzing, visualizing, and downloading data. We finish by discussing the main challenges and future opportunities for TELL and the speech biomarker field. With this effort, we hope to boost the use of digital speech markers in medical and research fields.
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