{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/coordvit-a-novel-method-of-improve-vision","title":"CoordViT: A Novel Method of Improve Vision Transformer-Based Speech Emotion Recognition using Coordinate Information Concatenate","arxiv_id":null,"date":"2023-03-10","proceeding":"International Conference on Electronics, Information, and Communication (ICEIC) 2023 3","authors":["Jeongyoon Kim","Seung-Ho Lee"],"abstract":"Recently, in speech emotion recognition, a Transformer-based method using spectrogram images instead of sound data showed improved accuracy than Convolutional Neural Networks (CNNs). Vision Transformer (ViT), a Transformer-based method, achieves high classification accuracy by using divided patches from the input image, but has a problem in that pixel position information is not retained due to embedding layers such as linear projection. Therefore, in this paper, we propose a novel method of improve ViT-based speech emotion recognition using coordinate information concatenate. Since the proposed method retains pixel position information by concatenating coordinate information to the input image, the accuracy of CREMA-D is greatly improved by 82.96% compared to the state-of-art about CREMA-D. As a result, it proved that the coordinate information concatenate proposed in this paper is effective not only for CNNs but also for Transformers.","url_abs":"https://ieeexplore.ieee.org/document/10049941","url_pdf":"https://ieeexplore.ieee.org/document/10049941","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":null,"task_name":"Position"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-emotion-recognition-on-crema-d","task":"Speech Emotion Recognition","dataset":"CREMA-D","model":"CoordViT","rank_in_archive_order":3,"of":9,"metrics":{"Accuracy":"82.96"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}