A multimodal approach for automatic assessment of school principals' oral presentation during pre-service training program

Hsiao, Shan-Wen; Sun, Hung-Ching; Hsieh, Ming-Chuan; Tsai, Ming-Hsueh; Lin, Hung-Yin; Lee, Chi-Chun

doi:10.21437/interspeech.2015-545

Interspeech 2015 2015

DOI: 10.21437/interspeech.2015-545

|View full text |Cite

A multimodal approach for automatic assessment of school principals' oral presentation during pre-service training program

Shan-Wen Hsiao

Hung-Ching Sun

Ming-Chuan Hsieh³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2016

Publication Types

Select...

Other1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhancement of Automatic Oral Presentation Assessment System Using Latent N-Grams Word Representation and Part-of-Speech Information

Huang¹,

Hsiao

Sun

et al. 2016

Interspeech 2016

Self Cite

View full text Add to dashboard Cite

The development of an automatic oral presentation assessment system is important for the educational researchers to assess and train the communication ability of school leaders. In this work, we aim at enhancing the performance of the existing pre-service school principals' presentation scoring system by including lexical information as an additional modality. We propose to use latent n-grams distributed word representations and weighted counts of part-of-speech tag to derive features from the speech transcripts in the National Academy for Educational Research (NAER) oral presentation database. We carry out two different experiments: Exp I is a binary classification task between high versus low performing speech, and Exp II is a continuous scoring on the entire dataset. In Exp I, the proposed framework achieves a competitive accuracy of 0.79, and in Exp II, by fusing this text-based system to the existing audio-video based system, we obtain a spearman correlation of 0.641 (18.05% relative improvement). The two experiments demonstrate the modeling power of our proposed framework and signify the substantial complementary information in the lexical modality while assessing the quality of an oral presentation.

show abstract

Enhancement of Automatic Oral Presentation Assessment System Using Latent N-Grams Word Representation and Part-of-Speech Information

Huang¹,

Hsiao

Sun

et al. 2016

Interspeech 2016

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A multimodal approach for automatic assessment of school principals' oral presentation during pre-service training program

Cited by 1 publication

References 0 publications

Enhancement of Automatic Oral Presentation Assessment System Using Latent N-Grams Word Representation and Part-of-Speech Information

Enhancement of Automatic Oral Presentation Assessment System Using Latent N-Grams Word Representation and Part-of-Speech Information

Contact Info

Product

Resources

About