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Abstract: Observational methods are fundamental to the study of human behavior in the behavioral sciences. For example, in the context of research on intimate relationships, psychologists’ hypotheses are often empirically tested by video recording interactions of couples and manually coding relevant behaviors using standardized coding systems. This coding process can be time-consuming, and the resulting coded data may have a high degree of variability because of a number of factors (e.g., inter-evaluator differences). These challenges provide an opportunity to employ engineering methods to aid in automatically coding human behavioral data. In this work, we analyzed a large corpus of married couples’ problem-solving interactions. Each spouse was manually coded with multiple session-level behavioral observations (e.g., level of blame toward other spouse), and we used acoustic speech features to automatically classify extreme instances for six selected codes (e.g., “low” vs. “high” blame). Specifically, we extracted prosodic, spectral, and voice quality features to capture global acoustic properties for each spouse and trained gender-specific and gender-independent classifiers. The best overall automatic system correctly classified 74.1% of the instances, an improvement of 3.95% absolute (5.63% relative) over our previously reported best results. We compare performance for the various factors: across codes, gender, classifier type, and feature type. [Copyright &y& Elsevier]

Abstract: To automate assessments of beginning readers, especially those still learning English, we have investigated the types of knowledge sources that teachers use and have tried to incorporate them into an automated system. We describe a set of speech recognition and verification experiments and compare teacher scores with automatic scores in order to decide when a novel pronunciation is best viewed as a reading error or as dialect variation. Since no one classroom teacher is expected to be familiar with as many dialect systems as might occur in an urban classroom, making progress in automated assessments in this area can improve the consistency and fairness of reading assessment. We found that automatic methods performed best when the acoustic models were trained on both native and non-native speech, and argue that this training condition is necessary for automatic reading assessment since a child’s reading ability is not directly observable in one utterance. We also found assessment of emerging reading skills in young children to be an area ripe for more research! [Copyright &y& Elsevier]