InfoSyll: A Syllabary Providing Statistical Information on Phonological and Orthographic Syllables

here is now a growing body of evidence in various languages supporting the claim that syllables are functional units of visual word processing. In the perspective of modeling the processing of polysyllabic words and the activation of syllables, current studies investigate syllabic effects with subtle manipulations. We present here a syllabary of the French language aiming at answering new constraints when designing experiments on the syllable issue. The InfoSyll syllabary provides exhaustive characteristics and statistical information for each phonological syllable (e.g. /fi/) and for its corresponding orthographic syllables (e.g. fi, phi, phy, fee, fix, fis). Variables such as the type and token positional frequencies, the number and frequencies of the correspondences between orthographic and phonological syllables are provided. As discussed, such computations should allow precise controls, manipulations and quantitative descriptions of syllabic variables in the field of psycholinguistic research.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder

There is a lack of consensus on the diagnostic thresholds that could improve the detection accuracy of bipolar mixed episodes in clinical settings. Some studies have shown that voice features could be reliable biomarkers of manic and depressive episodes compared to euthymic states, but none thus far have investigated whether they could aid the distinction between mixed and non-mixed acute bipolar episodes. Here we investigated whether vocal features acquired via verbal fluency tasks could accurately classify mixed states in bipolar disorder using machine learning methods. Fifty-six patients with bipolar disorder were recruited during an acute episode (19 hypomanic, 8 mixed hypomanic, 17 with mixed depression, 12 with depression). Nine different trials belonging to four conditions of verbal fluency tasks—letter, semantic, free word generation, and associational fluency—were administered. Spectral and prosodic features in three conditions were selected for the classification algorithm. Using the leave-one-subject-out (LOSO) strategy to train the classifier, we calculated the accuracy rate, the F1 score, and the Matthews correlation coefficient (MCC). For depression versus mixed depression, the accuracy and F1 scores were high, i.e., respectively 0.83 and 0.86, and the MCC was of 0.64. For hypomania versus mixed hypomania, accuracy and F1 scores were also high, i.e., 0.86 and 0.75, respectively, and the MCC was of 0.57. Given the high rates of correctly classified subjects, vocal features quickly acquired via verbal fluency tasks seem to be reliable biomarkers that could be easily implemented in clinical settings to improve diagnostic accuracy