ArticleAdvances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference2017
Speech Intention Classification with Multimodal Deep Learning.
Article in Advances in artificial intelligence. Canadian Society for Computational Studies of Intelligence. Conference, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Deep Learning-Based Classification of Spoken English Digits.Computational intelligence and neuroscience · 2022Article
- An Analysis of Speech during Life Saving Interventions to Inform the Design of a Computerized System for Delay Detection.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality.Sensors (Basel, Switzerland) · 2021Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Hybrid Attention based Multimodal Network for Spoken Language Classification.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2018Article
- Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2018Article
- An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork.International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare · 2018Article
- DEEP MULTIMODAL LEARNING FOR EMOTION RECOGNITION IN SPOKEN LANGUAGE.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2018Article
- Language-Based Process Phase Detection in the Trauma Resuscitation.Proceedings. IEEE International Conference on Healthcare Informatics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
We present a novel multimodal deep learning structure that automatically extracts features from textual-acoustic data for sentence-level speech classification. Textual and acoustic features were first extracted using two independent convolutional neural network structures, then combined into a joint representation, and finally fed into a decision softmax layer. We tested the proposed model in an actual medical setting, using speech recording and its transcribed log. Our model achieved 83.10% average accuracy in detecting 6 different intentions. We also found that our model using automatically extracted features for intention classification outperformed existing models that use manufactured features.
Indexed as
Identifiers
What 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.