Kenji Miyazaki
Research Subjects
Research Interests
Kenji Miyazaki has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Masakazu Higuchi
Graduate School of Health Innovation, Kanagawa University of Human Services, Yokosuka 238-8522, Japan
1 shared publication - Mitsuteru Nakamura
Graduate School of Health Innovation, Kanagawa University of Human Services, Yokosuka 241-0815, Japan
1 shared publication - Noriaki Sonota
Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
1 shared publication - Shinichi Tokuno
Graduate School of Health Innovation, Kanagawa University of Human Services, Yokosuka 238-8522, Japan
1 shared publication - Shuji Shinohara
School of Science and Engineering Tokyo Denki University Saitama Japan
1 shared publication - Shunji Mitsuyoshi
Department of Bioengineering, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
1 shared publication
Latest Publications
- Open Access
Performance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances
It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.
Open AccessPerformance Evaluation of a Voice-Based Depression Assessment System Considering the Number and Type of Input Utterances
It is empirically known that mood changes affect facial expressions and voices. In this study, the authors have focused on the voice to develop a method for estimating depression in individuals from their voices. A short input voice is ideal for applying the proposed method to a wide range of applications. Therefore, we evaluated this method using multiple input utterances while assuming a unit utterance input. The experimental results revealed that depressive states could be estimated with sufficient accuracy using the smallest number of utterances when positive utterances were included in three to four input utterances.