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Detecting Mild Cognitive Impairment by Exploiting Linguistic Information from Transcripts.

, , , , , , , and . ACL (2), The Association for Computer Linguistics, (2016)

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The use of speed-up techniques for a speech recognizer system., and . I. J. Speech Technology, 9 (3-4): 95-107 (2006)Improving the Multi-stack Decoding Algorithm in a Segment-Based Speech Recognizer., and . IEA/AIE, volume 2718 of Lecture Notes in Computer Science, page 744-749. Springer, (2003)Optimizing the Ultrasound Tongue Image Representation for Residual Network-Based Articulatory-to-Acoustic Mapping., , , , and . Sensors, 22 (22): 8601 (2022)Using Spectral Sequence-to-Sequence Autoencoders to Assess Mild Cognitive Impairment., , , , , , , , and . ICASSP, page 6467-6471. IEEE, (2022)Automatic recognition of temporal speech features in type 2 diabetes mellitus with mild cognitive impairment., , , , , , , and . CogInfoCom, page 27-28. IEEE, (2019)Identifying Schizophrenia Based on Temporal Parameters in Spontaneous Speech., , , , and . INTERSPEECH, page 3408-3412. ISCA, (2018)General Utterance-Level Feature Extraction for Classifying Crying Sounds, Atypical & Self-Assessed Affect and Heart Beats., , and . INTERSPEECH, page 531-535. ISCA, (2018)Neural Speaker Embeddings for Ultrasound-Based Silent Speech Interfaces., , , , and . Interspeech, page 1932-1936. ISCA, (2021)Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby & Orca Sounds.. INTERSPEECH, page 2413-2417. ISCA, (2019)Determining Native Language and Deception Using Phonetic Features and Classifier Combination., , , and . INTERSPEECH, page 2418-2422. ISCA, (2016)