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Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques.

, , , , , and . EURASIP J. Adv. Signal Process., (2009)

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Embodied conversational agents for voice-biometric interfaces., , , , and . ICMI, page 305-312. ACM, (2008)Driver Identification and Verification From Smartphone Accelerometers Using Deep Neural Networks., , and . IEEE Trans. Intell. Transp. Syst., 23 (1): 97-109 (2022)Data-Driven Performance Evaluation Framework for Multi-Modal Public Transport Systems., , , and . Sensors, 22 (1): 17 (2022)Analyzing GMMs to characterize resonance anomalies in speakers suffering from apnoea., , , , , and . INTERSPEECH, page 1459-1462. ISCA, (2009)Deep Neural Networks for Driver Identification Using Accelerometer Signals from Smartphones., , and . BIS (2), volume 354 of Lecture Notes in Business Information Processing, page 206-220. Springer, (2019)Severe Apnoea Detection using Speaker Recognition Techniques., , , , , and . BIOSIGNALS, page 124-130. INSTICC Press, (2009)Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment., , , , , and . Comput. Math. Methods Medicine, (2015)Characterization of COVID-19's Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain., , , and . Sensors, 21 (19): 6574 (2021)Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques., , , , , and . EURASIP J. Adv. Signal Process., (2009)Analyzing Training Dependencies and Posterior Fusion in Discriminant Classification of Apnea Patients Based on Sustained and Connected Speech., , , , and . INTERSPEECH, page 3033-3036. ISCA, (2011)