蘇明祥

助理教授
SuPHD (1)

蘇明祥   Su, Ming-Hsiang Assistant Professor

東吳大學資料科學系助理教授

研究室: Q218
校內分機 : 6477

現職

東吳大學資料科學系助理教授

研究領域

    • 口語對話系統
    • 自然語言處理
    • 情緒偵測與識別
    • 人格特質感知
    • 人工智慧
    • 數位學習

 

學歷

  • Ph.D., Computer Science and Information Engineering, National Chung Cheng University 2004-2013
  • M.S., National Pingtung University of Science and Technology 2001-2003
  • B.S., Computer Science, Tunghai University, 1998-2001

經歷

  • Postdoctoral researcher, Computer Science and Information Engineering, National Cheng Kung University, 2013-2020
  • Lecturer, Ministry of Labor, 2013
  • Adjunct Lecturer, Management Information System National Pingtung University of Science and Technology, 2012-2013
  • Adjunct Lecturer, Computer Science and Information Engineering, National Chung Cheng University, 2006-2013:
  • R & D Engineer, CINO Co., Ltd., 2003-2004
  • 論文
    • M.-H. Su, C.-H. Wu, and H.-T. Cheng, “A Two-Stage Transformer-Based Approach for Variable-Length Abstractive Summarization,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2061-2072, IEEE, 2020. (SCI, IF:2.95)
    • M.-H. Su, C.-H. Wu, and L.-Y. Chen, “Attention-based Response Generation Using Parallel Double Q-Learning for Dialog Policy Decision in a Conversational System,” IEEE/ACM Transactions on Audio, Speech and Language Processing, 28, 131-143, IEEE, 2019. (SCI, IF:2.95)
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, and T.-H. Yang, “Cell-Coupled Long Short-Term Memory With L-Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features,” IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2019.2899884, 2019. (SCI, IF:7.982)
    • Q.-B. Hong, C.-H. Wu, M.-H. Su, and C.-C. Chang, “Exploring Macroscopic and Microscopic Fluctuations of Elicited Facial Expressions for Mood Disorder Classification,” IEEE Transactions on Affective Computing, IEEE, 2019. (SCI, IF:4.585)
    • K.-Y. Huang, C.-H. Wu, and M.-H. Su, “Attention-based Convolutional Neural Network and Long Short-term Memory for Short-term Detection of Mood Disorders based on Elicited Speech Responses,” Pattern Recognition, 88, 668-678, Elsevier, 2019. (SCI, IF: 3.962)
    • M.-H. Su, C. -H. Wu, K.-Y. Huang, and W.-H. Lin, “Response Selection and Automatic Message-Response Expansion in Retrieval-Based QA Systems using Semantic Dependency Pair Model,” ACM Transactions on Asian and Low-Resource Language Information Processing, 18(1), 3:1-3:24, ACM, 2018. (SCI)
    • K.-Y. Huang, C.-H. Wu, M.-H. Su, and Y.-T. Kuo, “Detecting Unipolar and Bipolar Depressive Disorders from Elicited Speech Responses Using Latent Affective Structure,” IEEE Transactions on Affective Computing, DOI:10.1109/TAFFC.2018.2803178, IEEE, 2018. (SCI, IF:4.585)
    • C. -H. Wu, M. -H. Su, and W. -B. Liang, “Miscommunication handling in spoken dialog systems based on error-aware dialog state detection,” EURASIP Journal on Audio, Speech, and Music Processing, 1(9), 1-17, Springer, 2017. (SCI, IF:3.057)
    • T.-H. Yang, C.-H. Wu, K.-Y. Huang, M.-H. Su, “Coupled HMM-based multimodal fusion for mood disorder detection through elicited audio-visual signals,” Journal of Ambient Intelligence and Humanized Computing, 8(6), 895-906, Springer, 2017. (SCI, IF:1.423)
    • M.-H. Su, C.-H. Wu, and Y.-T. Zheng, “Exploiting Turn-Taking Temporal Evolution for Personality Trait Perception in Dyadic Conversations,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(4), 733-744, IEEE, 2016. (SCI, IF:2.95)
    • P.-T. Yu, B.-Y. Wang, and M.-H. Su, “Lecture capture with real time rearrangement of visual elements: ‐ impact on student performance,” Journal of Computer Assisted Learning, 31(6), 655-670, 2015. (SSCI, IF:1.859)
    • P.-T. Yu, Y.-H. Liao, M.-H. Su, “A Near-Reality Approach to Improve the e-Learning Open Courseware,” Journal of Educational Technology & Society, 16(4), 2013. (SSCI, IF:1.767)
    • C.-Y. Tsai, M.-H. Su, P.-T. Yu, “Based on the Concept of Learning Corner to Construct an Information TV System to Increase Student Learning Opportunities,” Journal of Convergence Information Technology, 8(8), 2013.
    • P.-Ta Yu, M.-H. Su, P.-J. Cheng, and Y.-H. Liao, “Applying Online Group Studies Environment to Enhance Student Reading Ability and Learning Effectiveness,” Journal of Internet Technology, 13(6), 981-988, 2012. (SCI, IF:1.301)
    • P.-T. Yu, Y.-H. Liao, M.-H. Su, P.-J. Cheng, and C.-H. Pai, “A Rapid Auto-Indexing Technology for Designing Readable e-Learning Content,” The International Review of Research in Open and Distance Learning, 13(5), pp. 20-38, 2012. (SSCI, IF:1.734)

  • 研討會論文
    • M.-H. Su, C.-H. Wu, and Y. Chang, “Follow-Up Question Generation using Neural Tensor Network-based Domain Ontology Population in an Interview Coaching System,” in Proceeding of INTERSPEECH, Graz, Austria, pp. 4185-4189, ISCA, September 2019.
    • M.-H. Su, C.-H. Wu and P.-C. Shih, “Automatic Ontology Population Using Deep Learning for Triple Extraction,” in Proceeding of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Lanzhou, China, pp. 262-267, IEEE, November 2019.
    • Q.-B. Hong, C.-H. Wu, M.-H. Su and H.-M. Wang, “Sequential Speaker Embedding and Transfer Learning for Text-Independent Speaker Identification,” in Proceeding of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Lanzhou, China, pp. 827-832, IEEE, November 2019.
    • K.-Y. Huang, C.-H. Wu, Q.-B. Hong, M.-H. Su, and Y.-H. Chen, “Speech Emotion Recognition Using Deep Neural Network Considering Verbal and Nonverbal Speech Sounds,” in Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, United Kingdom, pp. 5866-5870, IEEE, May 2019.
    • K.-Y. Huang, C.-H. Wu, Q.-B. Hong, M.-H. Su, and Y.-R. Zeng, “Speech Emotion Recognition using Convolutional Neural Network with Audio Word-based Embedding,” in Proceeding of the 11th International Symposium on Chinese Spoken Language Processing (ISCSLP), Taipei, Taiwan, November 2018.
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, Q.-B. Hong, and H.-H. Huang, “Follow-up Question Generation using Pattern-based Seq2seq with a Small Corpus for Interview Coaching,” in Proceeding of INTERSPEECH, Hyderabad, India, pp. 1006-1010, ISCA, September 2018.
    • M.-H. Su, C. H. Wu, K. Y. Huang, and Q. B. Hong, “LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors,” in Proceeding of 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), Beijing, China, IEEE, May 2018.
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, and C.-K. Chen, “Attention-based Dialog State Tracking for Conversational Interview Coaching,” in Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, Alberta, Canada, pp. 6144-6148, IEEE, April 2018.
    • K.-Y. Huang, C.-H. Wu, M.-H. Su, and C.-H. Chou, “Mood disorder identification using deep bottleneck features of elicited speech,” in Proceeding of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia, pp. 1648-1652, IEEE, December 2017.
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, Q.-B. Hong, and H.-M. Wang, “Personality trait perception from speech signals using multiresolution analysis and convolutional neural networks,” in Proceeding of Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Kuala Lumpur, Malaysia, pp. 1532-1536, IEEE, December 2017.
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, Q.-B. Hong, and H.-M. Wang, “A Chatbot Using LSTM-based Multi-Layer Embedding for Elderly Care,” in Proceeding of International Conference on Orange Technologies, Singapore, pp. 70-74, IEEE, December 2017.
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, Q.-B. Hong, and H.-M. Wang, “Exploring Microscopic Fluctuation of Facial Expression for Mood Disorder Classification,” in Proceeding of International Conference on Orange Technology, Singapore, pp. 65-69, IEEE, December 2017.
    • K.-Y. Huang, C.-H. Wu, M.-H. Su, and H.-C. Fu (2016, Dec). Mood Detection from Daily Conversational Speech Using Denoising Autoencoder and LSTM,” in Proceeding of International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, USA, pp. 5125-5129, IEEE, March 2017.
    • M.-H. Su, C.-H. Wu, K.-Y. Huang, T.-H. Yang, and T.-C. Huang, “Dialog State Tracking for Interview Coaching Using Two-Level LSTM,” in Proceeding of the 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), Tianjin, China, IEEE, October 2016
    • T.-H. Yang, C.-H. Wu, K.-Y. Huang, and M.-H. Su, “Detection of Mood Disorder Using Speech Emotion Profiles and LSTM,” in Proceeding of the 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), Tianjin, China, IEEE, October 2016.
    • M.-H. Su, K.-Y. Huang, T.-H. Yang, K.-J. Lai and C.-H. Wu, “Dialog State Tracking and Dialog Action Detection Using Deep Learning Mechanism for Interview Coaching,” in Proceeding of the 20th International Conference on Asian Language Processing (IALP), Tainan, Taiwan, IEEE, November 2016.
    • M.-H. Su, W.-H. Lin, T.-H. Yang, and C.-H. Wu, “Automatic Text Segmentation from Unstructured Data Using LDA and Delta-BIC,” in Proceeding of the conference of 2015 National Computer Symposium, Pingtung, Taiwan, 2015.
    • M.-H. Su, Y.-T. Zheng, and C.-H. Wu, “Interlocutor Personality Perception based on BFI Profiles and Coupled HMMs in a Dyadic Conversation,” in Proceeding of the conference of International Symposium on Chinese Spoken Language Processing (ISCSLP), Singapore, IEEE, September 2014.

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