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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Hybrid Attention based Multimodal Network for Spoken Language Classification.

Yue Gu¹, Kangning Yang¹, Shiyu Fu¹

¹Multimedia Image Processing Lab, Electrical and Computer Engineering Department, Rutgers University, Piscataway, NJ, USA.

Proceedings of the Conference. Association for Computational Linguistics. Meeting

|November 10, 2018

Summary

This summary is machine-generated.

This study introduces a deep multimodal network for speech understanding, utilizing linguistic and vocal features. The hybrid attention model achieves top results on various datasets, including medical speech analysis.

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Area of Science:

Artificial Intelligence
Natural Language Processing
Speech Recognition

Background:

Human spoken language understanding relies on integrating linguistic content and vocal characteristics.
Current deep learning models require effective methods for multimodal fusion and feature extraction.
Analyzing speech data, especially in critical scenarios like medical emergencies, presents unique challenges.

Purpose of the Study:

To investigate the utility of linguistic and vocal features for multimodal deep learning in speech understanding.
To propose and evaluate a novel deep multimodal network with feature and modality attention.
To assess the system's performance on diverse datasets, including real-world medical speech data.

Main Methods:

Development of a deep multimodal network incorporating feature attention and modality attention mechanisms.
Training and evaluation of the proposed architecture on three benchmark multimodal speech datasets.
Testing the system's generalization capabilities on a specialized medical speech dataset from a trauma scenario.
Comparative analysis of deep learning methods against traditional approaches for feature extraction and fusion.

Main Results:

The proposed hybrid attention architecture achieved state-of-the-art or competitive performance across multiple multimodal datasets.
The system demonstrated effectiveness and generalization, particularly on the challenging medical speech dataset.
The study provided a comprehensive comparison of deep learning and traditional methods for speech data analysis.

Conclusions:

The developed deep multimodal network effectively leverages linguistic and vocal cues for enhanced speech understanding.
Hybrid attention mechanisms are crucial for informative representation learning and model fusion in multimodal systems.
The approach shows significant promise for applications in diverse fields, including medical informatics and human-computer interaction.