ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2018
DEEP MULTIMODAL LEARNING FOR EMOTION RECOGNITION IN SPOKEN LANGUAGE.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Empowering emotional intelligence through deep learning techniques.Scientific reports · 2025Article
- Multimodal fusion-powered English speaking robot.Frontiers in neurorobotics · 2024Article
- Effect on speech emotion classification of a feature selection approach using a convolutional neural network.PeerJ. Computer science · 2021Article
- Affective Latent Representation of Acoustic and Lexical Features for Emotion Recognition.Sensors (Basel, Switzerland) · 2020Article
- 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
- Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network.Sensors (Basel, Switzerland) · 2019Article
- Human Conversation Analysis Using Attentive Multimodal Networks with Hierarchical Encoder-Decoder.Proceedings of the ... ACM International Conference on Multimedia, with co-located Symposium & Workshops. ACM International Conference on Multimedia · 2018Article
- 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
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In this paper, we present a novel deep multimodal framework to predict human emotions based on sentence-level spoken language. Our architecture has two distinctive characteristics. First, it extracts the high-level features from both text and audio via a hybrid deep multimodal structure, which considers the spatial information from text, temporal information from audio, and high-level associations from low-level handcrafted features. Second, we fuse all features by using a three-layer deep neural network to learn the correlations across modalities and train the feature extraction and fusion modules together, allowing optimal global fine-tuning of the entire structure. We evaluated the proposed framework on the IEMOCAP dataset. Our result shows promising performance, achieving 60.4% in weighted accuracy for five emotion categories.
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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.