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Automatic recognition of temporal speech features in type 2 diabetes mellitus with mild cognitive impairment.

, , , , , , , and . CogInfoCom, page 27-28. IEEE, (2019)

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Social Signal Detection by Probabilistic Sampling DNN Training., , and . IEEE Trans. Affect. Comput., 11 (1): 164-177 (2020)Replicator Neural Networks for Outlier Modeling in Segmental Speech Recognition., and . ISNN (1), volume 3173 of Lecture Notes in Computer Science, page 996-1001. Springer, (2004)Reducing the Inter-speaker Variance of CNN Acoustic Models Using Unsupervised Adversarial Multi-task Training., and . SPECOM, volume 11658 of Lecture Notes in Computer Science, page 481-490. Springer, (2019)Assessing Alzheimer's Disease from Speech Using the i-vector Approach., , , , , and . SPECOM, volume 11658 of Lecture Notes in Computer Science, page 289-298. Springer, (2019)Aggregation Operators and Hypothesis Space Reductions in Speech Recognition., and . TSD, volume 3206 of Lecture Notes in Computer Science, page 315-322. Springer, (2004)Speeding Up Dynamic Search Methods in Speech Recognition., and . IEA/AIE, volume 3533 of Lecture Notes in Computer Science, page 98-100. Springer, (2005)Spoken term detection from noisy input., , and . SACI, page 91-96. IEEE, (2011)Improving speed and accuracy in automatic speech recognition. University of Szeged, Hungary, (2010)Estimating the Sincerity of Apologies in Speech by DNN Rank Learning and Prosodic Analysis., , , and . INTERSPEECH, page 2026-2030. ISCA, (2016)DNN-Based Ultrasound-to-Speech Conversion for a Silent Speech Interface., , , , and . INTERSPEECH, page 3672-3676. ISCA, (2017)