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Purpose:The quality of problem representation is critical for developing students’ problem-solving abilities in problem-based learning (PBL). This study investigates preclinical students’ experience with standardized patients (SPs) as a problem representation method compared to using video cases in PBL.Methods:A cohort of 99 second-year preclinical students from Inje University College of Medicine (IUCM) responded to a Likert scale questionnaire on their learning experiences after they had experienced both video cases and SPs in PBL. The questionnaire consisted of 14 items with eight subcategories: problem identification, hypothesis generation, motivation, collaborative learning, reflective thinking, authenticity, patient-doctor communication, and attitude toward patients.Results:The results reveal that using SPs led to the preclinical students having significantly positive experiences in boosting patient-doctor communication skills; the perceived authenticity of their clinical situations; development of proper attitudes toward patients; and motivation, reflective thinking, and collaborative learning when compared to using video cases. The SPs also provided more challenges than the video cases during problem identification and hypotheses generation.Conclusion:SPs are more effective than video cases in delivering higher levels of authenticity in clinical problems for PBL. The interaction with SPs engages preclinical students in deeper thinking and discussion; growth of communication skills; development of proper attitudes toward patients; and motivation. Considering the higher cost of SPs compared with video cases, SPs could be used most advantageously during the preclinical period in the IUCM curriculum.

Voice conversion (VC) is a task to transform a person's voice to different style while conserving linguistic contents. Previous state-of-the-art on VC is based on sequence-to-sequence (seq2seq) model, which could mislead linguistic information. There was an attempt to overcome it by using textual supervision, it requires explicit alignment which loses the benefit of using seq2seq model. In this paper, a voice converter using multitask learning with text-to-speech (TTS) is presented. The embedding space of seq2seq-based TTS has abundant information on the text. The role of the decoder of TTS is to convert embedding space to speech, which is same to VC. In the proposed model, the whole network is trained to minimize loss of VC and TTS. VC is expected to capture more linguistic information and to preserve training stability by multitask learning. Experiments of VC were performed on a male Korean emotional text-speech dataset, and it is shown that multitask learning is helpful to keep linguistic contents in VC.

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