A Simple Attention-Based Mechanism for Bimodal Emotion Classification

• URL: http://arxiv.org/abs/2407.00134v1
• Date: Fri, 28 Jun 2024 10:43:02 GMT
• Title: A Simple Attention-Based Mechanism for Bimodal Emotion Classification
• Authors: Mazen Elabd, Sardar Jaf,
• Abstract summary: We present novel bimodal deep learning-based architectures enhanced with attention mechanism trained and tested on text and speech data for emotion classification. Our finding suggests that deep learning based architectures trained on different types of data (text and speech) outperform architectures trained only on text or speech.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Big data contain rich information for machine learning algorithms to utilize when learning important features during classification tasks. Human beings express their emotion using certain words, speech (tone, pitch, speed) or facial expression. Artificial Intelligence approach to emotion classification are largely based on learning from textual information. However, public datasets containing text and speech data provide sufficient resources to train machine learning algorithms for the tack of emotion classification. In this paper, we present novel bimodal deep learning-based architectures enhanced with attention mechanism trained and tested on text and speech data for emotion classification. We report details of different deep learning based architectures and show the performance of each architecture including rigorous error analyses. Our finding suggests that deep learning based architectures trained on different types of data (text and speech) outperform architectures trained only on text or speech. Our proposed attention-based bimodal architecture outperforms several state-of-the-art systems in emotion classification.

Related papers

• VLLMs Provide Better Context for Emotion Understanding Through Common Sense Reasoning [66.23296689828152]
  We leverage the capabilities of Vision-and-Large-Language Models to enhance in-context emotion classification. In the first stage, we propose prompting VLLMs to generate descriptions in natural language of the subject's apparent emotion. In the second stage, the descriptions are used as contextual information and, along with the image input, are used to train a transformer-based architecture.
  arXiv  Detail & Related papers  (2024-04-10T15:09:15Z)
• Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems [7.207019635697126]
  This article aims to determine which and where information is located in an automatic speech recognition acoustic model (AM) Experiments are performed on speaker verification, acoustic environment classification, gender classification, tempo-distortion detection systems and speech sentiment/emotion identification. Analysis showed that neural-based AMs hold heterogeneous information that seems surprisingly uncorrelated with phoneme recognition.
  arXiv  Detail & Related papers  (2024-02-29T18:43:53Z)
• Speech and Text-Based Emotion Recognizer [0.9168634432094885]
  We build a balanced corpus from publicly available datasets for speech emotion recognition. Our best system, a multi-modal speech, and text-based model, provides a performance of UA(Unweighed Accuracy) + WA (Weighed Accuracy) of 157.57 compared to the baseline algorithm performance of 119.66.
  arXiv  Detail & Related papers  (2023-12-10T05:17:39Z)
• Text Classification: A Perspective of Deep Learning Methods [0.0679877553227375]
  This paper introduces deep learning-based text classification algorithms, including important steps required for text classification tasks. At the end of the article, different deep learning text classification methods are compared and summarized.
  arXiv  Detail & Related papers  (2023-09-24T21:49:51Z)
• Accurate Emotion Strength Assessment for Seen and Unseen Speech Based on Data-Driven Deep Learning [70.30713251031052]
  We propose a data-driven deep learning model, i.e. StrengthNet, to improve the generalization of emotion strength assessment for seen and unseen speech. Experiments show that the predicted emotion strength of the proposed StrengthNet is highly correlated with ground truth scores for both seen and unseen speech.
  arXiv  Detail & Related papers  (2022-06-15T01:25:32Z)
• Self-Supervised Speech Representation Learning: A Review [105.1545308184483]
  Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. This review presents approaches for self-supervised speech representation learning and their connection to other research areas.
  arXiv  Detail & Related papers  (2022-05-21T16:52:57Z)
• data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language [85.9019051663368]
  data2vec is a framework that uses the same learning method for either speech, NLP or computer vision. The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup. Experiments on the major benchmarks of speech recognition, image classification, and natural language understanding demonstrate a new state of the art or competitive performance.
  arXiv  Detail & Related papers  (2022-02-07T22:52:11Z)
• Multimodal Emotion Recognition with High-level Speech and Text Features [8.141157362639182]
  We propose a novel cross-representation speech model to perform emotion recognition on wav2vec 2.0 speech features. We also train a CNN-based model to recognize emotions from text features extracted with Transformer-based models. Our method is evaluated on the IEMOCAP dataset in a 4-class classification problem.
  arXiv  Detail & Related papers  (2021-09-29T07:08:40Z)
• Sentiment analysis in tweets: an assessment study from classical to modern text representation models [59.107260266206445]
  Short texts published on Twitter have earned significant attention as a rich source of information. Their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks. This study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets.
  arXiv  Detail & Related papers  (2021-05-29T21:05:28Z)
• An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation [57.68765353264689]
  Speech enhancement and speech separation are two related tasks. Traditionally, these tasks have been tackled using signal processing and machine learning techniques. Deep learning has been exploited to achieve strong performance.
  arXiv  Detail & Related papers  (2020-08-21T17:24:09Z)
• Deep Learning Approach for Enhanced Cyber Threat Indicators in Twitter Stream [3.7354197654171797]
  This work proposes a deep learning based approach for tweet data analysis. To convert the tweets into numerical representations, various text representations are employed. For comparative analysis, the classical text representation method with classical machine learning algorithm is employed.
  arXiv  Detail & Related papers  (2020-03-31T00:29:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.