Comparison of Acoustic Parameters of Voice and Speech According to Vowel Type and Suicidal Risk in Adolescents.

Globally, suicide prevention and understanding suicidal behavior represent significant health challenges. The predictive potential of voice, speech, and language appears as a promising solution to the difficulty in assessment. OBJECTIVE: To analyze variations in acoustic parameters in voice and speech based on vowel types according to different levels of suicidal risk among adolescents in a text reading task. METHODOLOGY: Cross-sectional analytical design using nonprobabilistic sampling. Our sample comprised 98 adolescents aged 14 to 19, undergoing voice acoustic assessment, along with suicidal ideation determination through the Okasha Suicidality Scale and Beck Depression Inventory. Acoustic analysis of recordings was conducted using Praat for phonetic research, Python program, Focusrite interface, and microphone to register voice and speech acoustic parameters such as Fundamental Frequency, Jitter, and Formants. Subsequently, data from adolescents with and without suicidal risk were compared. RESULTS: Significant differences were observed between suicidal and nonsuicidal adolescents in several acoustic aspects, especially in females in fundamental frequency (F0), signal-to-noise ratio (HNRdB), and temporal variability measured by jitter and standard deviation. In men, differences were found in F0 and HNRdB (P < 0.05). CONCLUSION: This study demonstrated statistically significant variations in various voice acoustic parameters among adolescents with and without suicidal risk. These findings underscore the potential relevance of voice and speech as markers for suicidal risk.

Do Written Responses to Open-Ended Questions on Fourth-Grade Online Formative Assessments in Mathematics Help Predict Scores on End-of-Year Standardized Tests?

Predicting long-term student achievement is a critical task for teachers and for educational data mining. However, most of the models do not consider two typical situations in real-life classrooms. The first is that teachers develop their own questions for online formative assessment. Therefore, there are a huge number of possible questions, each of which is answered by only a few students. Second, online formative assessment often involves open-ended questions that students answer in writing. These types of questions in online formative assessment are highly valuable. However, analyzing the responses automatically can be a complex process. In this paper, we address these two challenges. We analyzed 621,575 answers to closed-ended questions and 16,618 answers to open-ended questions by 464 fourth-graders from 24 low socioeconomic status (SES) schools. Using regressors obtained from linguistic features of the answers and an automatic incoherent response classifier, we built a linear model that predicts the score on an end-of-year national standardized test. We found that despite answering 36.4 times fewer open-ended questions than closed questions, including features of the students' open responses in our model improved our prediction of their end-of-year test scores. To the best of our knowledge, this is the first time that a predictor of end-of-year test scores has been improved by using automatically detected features of answers to open-ended questions on online formative assessments.