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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Evaluating and Validating Emotion Elicitation Using English and Arabic Movie Clips on a Saudi Sample Alghowinem, Sharifa; Goecke, Roland; Wagner, Michael. - : MDPI, 2019 BASE Show details 2 From joyous to clinically depressed: Mood detection using spontaneous speech Alghowinem, Sharifa; Goecke, Roland; Wagner, Michael; Epps, Julian; Breakspear, Michael; Parker, Gordon In: Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25 (2015) Abstract: Depression and other mood disorders are common and disabling disorders. We present work towards an objective diagnostic aid supporting clinicians using affective sensing technology with a focus on acoustic and statistical features from spontaneous speech. This work investigates differences in expressing positive and negative emotions in depressed and healthy control subjects as well as whether initial gender classification increases the recognition rate. To this end, spontaneous speech from interviews of 30 subjects of each depressed and controls was analysed, with a focus on questions eliciting positive and negative emotions. Using HMMs with GMMs for classification with 30-fold cross-validation, we found that MFCC, energy and intensity features gave highest recognition rates when female and male subjects were analysed together. When the dataset was first split by gender, log energy and shimmer features, respectively, were found to give the highest recognition rates in females, while it was loudness for males. Overall, correct recognition rates from acoustic features for depressed female subjects were higher than for male subjects. Using temporal features, we found that the response time and average syllable duration were longer in depressed subjects, while the interaction involvement and articulation rate wesre higher in control subjects. Keyword: Artificial intelligenc; Cross validation; Data sets; Gender classification; Healthy controls; In-control; Keywords: Acoustic features; Mood detection; Mood disorders; Recognition rates; Sensing technology; Spontaneous speech; Statistical features; Temporal features URL: http://hdl.handle.net/1885/68868 BASE Hide details 3 From joyous to clinically depressed: Mood detection using spontaneous speech Alghowinem, Sharifa; Goecke, Roland; Wagner, Michael... In: Proceedings of the 25th International Florida Artificial Intelligence Research Society Conference, FLAIRS-25 (2015) BASE Show details 4 Design of an emotion elicitation framework for Arabic speakers Alghowinem, Sharifa; Alghuwinem, Sarah; AlShehri, Majdah... In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015) BASE Show details 5 Design of an emotion elicitation framework for Arabic speakers Alghowinem, Sharifa; Alghuwinem, Sarah; AlShehri, Majdah... In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2015) BASE Show details 6 The Big Australian Speech Corpus (The Big ASC) Chetty, Girija; Cassidy, Stephen; Butcher, Andrew Richard. - : Causal Productions, 2010 BASE Show details 7 A Blueprint for a comprehensive Australian English auditory-visual speech corpus Burnham, Denis; Ambikairajah, Eliathamby; Cutler, Anne. - : Somerville, MA : Cascadilla Proceedings Project, 2009 BASE Show details 8 A blueprint for a comprehensive Australian English auditory-visual speech corpus Ishihara, Shunichi; Fletcher, Janet Mary; Kemp, Nenagh. - : Cascadilla Proceedings Project, 2009 BASE Show details

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