Project Summary/Abstract The long-term objectives of the parent R01 project (R01DC016621, 08/15/2019 – 06/31/2024) are to obtain a deeper understanding how articulatory movement patterns are mapped to speech particularly when there is no vocal fold vibration (silent speech) and then to develop a novel, wearable assistive technology called silent speech interface (SSI) to assist the impaired oral communication for individuals in need (e.g., individuals after laryngectomy, surgical removal of larynx to treat advanced laryngeal cancer). The parent R01 project aims to (1) determine the articulatory patterns of normal (vocalized) and silent speech, produced by both healthy talkers and people after laryngectomy, (2) develop a wearable device for real-time tongue and lip motion tracking, and (3) synthesize speech from articulation directly. If successful, the proposed research will enhance human health by making an impact on individuals after laryngectomy and potentially to a broader range of other speech and voice disorders as well as visual feedback-based secondary language training and speech therapy. Through the parent R01 project, we are collecting a unique multi-modal speech dataset from patients following laryngectomy and healthy controls. This data set includes speech kinematics from multiple tracers attached on the tongue and the lips, and speech acoustics. Each tracer comprises multiple sensors that measure inertial and magnetic information that can provide additional information to assist the speech acoustic and the articulation-to-speech algorithm. Making such data ML ready for others to consume was out of scope due to required effort and complexity needed to pre-process and synchronize sampling between kinematic and acoustic data. The specific goal of this supplemental project is to make data AI/ML ready by developing the pre-processing algorithms needed to generate a set of features, along with proper formatting and labeling, that can be more easily shared through repositories and used by others to evaluate different ML algorithms. New ML models that will be tested on these ML-ready shared datasets will significantly advance our capabilities to translate articulatory motion into speech sounds, which will not only improve the quality of life for people affected by laryngectomy but also for the millions of individuals living with speech sound disorders such as Parkinson’s disease, and amyotrophic lateral sclerosis.