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Capturing Long-Term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition.

, , , , and . INTERSPEECH, page 1253-1257. ISCA, (2017)

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Emotion Recognition from Natural Phone Conversations in Individuals with and without Recent Suicidal Ideation., , , and . INTERSPEECH, page 3282-3286. ISCA, (2019)Into the Wild: Transitioning from Recognizing Mood in Clinical Interactions to Personal Conversations for Individuals with Bipolar Disorder., , and . INTERSPEECH, page 1438-1442. ISCA, (2019)Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech., , , , , , and . ICASSP, page 4858-4862. IEEE, (2014)Capturing Long-Term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition., , , , and . INTERSPEECH, page 1253-1257. ISCA, (2017)The PRIORI Emotion Dataset: Linking Mood to Emotion Detected In-the-Wild., , , , and . INTERSPEECH, page 1903-1907. ISCA, (2018)Recognition of Depression in Bipolar Disorder: Leveraging Cohort and Person-Specific Knowledge., , , and . INTERSPEECH, page 1215-1219. ISCA, (2016)Trainable Time Warping: Aligning Time-series in the Continuous-time Domain., , and . ICASSP, page 3502-3506. IEEE, (2019)Capturing Mismatch between Textual and Acoustic Emotion Expressions for Mood Identification in Bipolar Disorder., , , , and . INTERSPEECH, page 1718-1722. ISCA, (2023)Identifying Mood Episodes Using Dialogue Features from Clinical Interviews., , , , and . INTERSPEECH, page 1926-1930. ISCA, (2019)Mood state prediction from speech of varying acoustic quality for individuals with bipolar disorder., , and . ICASSP, page 2359-2363. IEEE, (2016)