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Effectiveness of Voice Quality Features in Detecting Depression.

, , , , , and . INTERSPEECH, page 1676-1680. ISCA, (2018)

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Algebra-Based Identification of Tree Patterns in XQuery., , , , and . FQAS, volume 4027 of Lecture Notes in Computer Science, page 13-25. Springer, (2006)Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion., , , , , , , and . CoRR, (2020)Voice Quality and Between-Frame Entropy for Sleepiness Estimation., , , and . INTERSPEECH, page 2408-2412. ISCA, (2019)Effectiveness of Voice Quality Features in Detecting Depression., , , , , and . INTERSPEECH, page 1676-1680. ISCA, (2018)ULoad: Choosing the Right Storage for Your XML Application., , , and . VLDB, page 1330-1333. ACM, (2005)Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification., , , , and . INTERSPEECH, page 766-770. ISCA, (2020)Time-Resolved Far Infrared Light Transport Decomposition for Thermal Photometric Stereo., , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 43 (6): 2075-2085 (2021)Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals., , , and . INTERSPEECH, page 2018-2022. ISCA, (2022)Fraug: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals., , , and . ICASSP, page 6267-6271. IEEE, (2022)A Privacy-preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech (short paper)., , , and . ML4CMH@AAAI, volume 3649 of CEUR Workshop Proceedings, page 57-63. CEUR-WS.org, (2024)