ArticleProceedings of the conference. Association for Computational Linguistics. Meeting2018
Hybrid Attention based Multimodal Network for Spoken Language Classification.
Article in Proceedings of the conference. Association for Computational Linguistics. Meeting, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Filamentary Convolution for SLI: A Brain-Inspired Approach with High Efficiency.Sensors (Basel, Switzerland) · 2025Article
- Multimodal Emotion Recognition from Art Using Sequential Co-Attention.Journal of imaging · 2021Article
- Mutual Correlation Attentive Factors in Dyadic Fusion Networks for Speech Emotion Recognition.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2019Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
We examine the utility of linguistic content and vocal characteristics for multimodal deep learning in human spoken language understanding. We present a deep multimodal network with both feature attention and modality attention to classify utterance-level speech data. The proposed hybrid attention architecture helps the system focus on learning informative representations for both modality-specific feature extraction and model fusion. The experimental results show that our system achieves state-of-the-art or competitive results on three published multimodal datasets. We also demonstrated the effectiveness and generalization of our system on a medical speech dataset from an actual trauma scenario. Furthermore, we provided a detailed comparison and analysis of traditional approaches and deep learning methods on both feature extraction and fusion.
Identifiers
30410219PMC6217979W2868391394What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.