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ANALYSIS OF MULTIMODAL FEATURES FOR SPEAKING PROFICIENCY SCORING IN AN INTERVIEW DIALOGUE Mao Saeki1 , Yoichi Matsuyama1 , Satoshi Kobashikawa2 , Tetsuji Ogawa1 , Tetsunori Kobayashi1 1Department of Communications and Computer Engineering, Waseda University, Japan 2 NTT Media Intelligence Laboratories, NTT Corporation, Japan saeki,matsuyama,ogawa@pcl.cs.waseda.ac.jp satoshi.kobashikawa.he@hco.ntt.co.jp, kobas@waseda.jpABSTRACT This paper analyzes the effectiveness of different modalities in
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Identify the multimodal features you want to analyze, such as text, audio, visual, or sensory data.
02
Collect relevant data for each identified feature.
03
Preprocess the data to ensure it is clean and formatted correctly for analysis.
04
Select appropriate analytical methods or tools to analyze each feature individually.
05
Integrate the findings from the analysis of each feature to identify patterns or correlations.
06
Interpret the results in the context of your research or project goals.
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Who needs analysis of multimodal features?

01
Researchers studying human-computer interaction.
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Educators developing multimodal teaching strategies.
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Marketers analyzing consumer behavior across different media.
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Data scientists working on complex datasets involving multiple modalities.
05
Healthcare professionals analyzing patient data from various sources.
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Analysis of multimodal features involves examining different forms of data and signals, such as visual, auditory, and textual information, to gain insights or make decisions based on a comprehensive understanding of complex systems.
Generally, researchers, analysts, or organizations that utilize multiple types of data in their studies or projects are required to file an analysis of multimodal features, particularly in fields such as machine learning, data science, and cognitive research.
To fill out an analysis of multimodal features, one must gather relevant data from different modalities, document the methods and tools used for analysis, report the findings, and ensure clarity in presenting how each feature contributes to the overall analysis.
The purpose of analysis of multimodal features is to leverage diverse types of information to enhance understanding, improve accuracy in predictions, and enable more informed decisions in various applications.
The analysis of multimodal features must report data sources, methodologies used, results or findings from the analysis, interpretations of how different modalities interrelate, and any conclusions drawn from the study.
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