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Enhancing Speech Emotion Recognition for Real-World Applications via ASR Integration 11

Enhancing Speech Emotion Recognition for Real-World Applicat...

引用

11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Li, Yuanchao Univ Edinburgh Ctr Speech Technol Res Edinburgh Midlothian Scotland

ISBN: (纸本)9798350327458

Speech Emotion Recognition (SER) is a major focus of affective computing and has developed rapidly over the past few decades. However, the lack of large-scale emotional speech data has hindered the application of SER. As a result, most SER research conducted on existing corpora in the lab is not applicable to the wild. To address this issue, we propose integrating Automatic Speech Recognition (ASR), which is widely used in the wild, into SER for real-world applications. Specifically, we aim to investigate the mutual impact of speech and emotion recognition to understand how ASR performs on emotional speech and leverage the representations and transcripts from ASR models to develop a robust SER model. this research is expected to alleviate the data scarcity problem in SER and enable its use in various real-world applications. Furthermore, our findings can expand the usage of ASR in complicated speech scenarios (e.g., emotional speech) and advance other speech tasks (e.g., recognizing affective and health states) that face similar issues as SER.

关键词： speech emotion recognition self-supervised learning semi-supervised learning multimodal fusion ASR

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Optimization of Logistic Regression Model by Feature engineering using Fuzzy Logic 18

Optimization of Logistic Regression Model by Feature Enginee...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rohit Kumar, Vivek Abes Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9789380544519

In modern era, the Machine learning especially Supervised Machine learning is iridescent field where many researchers are shaping their research work. Innovations and new ideas in field are growing fast. Still in various fields like medical, financial and other, we need to enhance the predictions of the Logistic Regression classification models. In this research paper, we focused on the optimization of the supervised Logistic Regression model which can predict more accurately when implemented on clinical dataset. Our purpose is to introduce and implement the fuzzy logic for Features engineering technique to optimize the logistic regression model before training of the Logistic regression, which can predict more accurately in the medical data sets. In this paper, we use Coronary Heart Disease (CHD) for implementing the features engineering using fuzzy logic. Final result showed that Optimized Logistic Regression Model prediction of Ten Years Heart risk is improved with accuracy of. 863 when classical Logistic Regression classification model has been compared with it. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Coronary heart disease Feature engineering Fuzzy logic Logistic regression Machine learning

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Transformer-based Self-supervised Representation learning for Emotion Recognition Using Bio-signal Feature Fusion 11

Transformer-based Self-supervised Representation Learning fo...

引用

11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Sawant, Shrutika S. Erick, F. X. Arora, Pulkit Pahl, Jaspar Foltyn, Andreas Holzer, Nina Gotz, theresa Fraunhofer Inst Integrated Circuits IIS Sensory Percept & Analyt Erlangen Germany

ISBN: (纸本)9798350327458

In this paper, we present a new emotion recognition framework that utilizes transformer based self-supervised representations from different bio-signals and combines them into a fused representation for the task of emotion recognition. Specifically, we explore a cross-attention based fusion mechanism that can explore mutual features among different bio-signals and learn more meaningful embeddings to estimate emotions effectively. Extensive experiments on a public dataset WESAD outperform the performance of fully supervised method for emotion recognition tasks and demonstrate the benefits of self-supervised features in recognizing different emotions. We also present a series of ablation studies to validate the proposed approach.

关键词： affective computing emotion recognition fusion multimodal self-supervised learning transformer

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"It's not Fair!" - Fairness for a Small dataset of Multi-modal Dyadic Mental Well-being Coaching 11

"It's not Fair!" - Fairness for a Small Dataset of Multi-mod...

引用

11th international conference on Affective Computing and intelligent Interaction (ACIIW)

作者： Cheong, Jiaee Spitale, Micol Gunes, Hatice Univ Cambridge Cambridge England

ISBN: (纸本)9798350327434

In recent years, the affective computing research community has put ethics at the centre of its research agenda. However, many of the currently available datasets for affective computing are 'small', making bias and debias analysis challenging. this paper presents the first work to explore bias analysis and mitigation of a small temporal multi-modal dataset for mental well-being by adopting different data augmentation techniques. this proof-of-concept work's contributions include: i) introducing a novel small temporal multi-modal dataset of dyadic interactions during mental well-being coaching;ii) providing multi-modal and feature importance analyses evaluated via modelling performance and fairness metrics across both high and low-level features;and iii) proposing a simple and effective data augmentation strategy (MixFeat) to debias the small dataset presented in this paper. We conduct extensive experiments and analyses to compare our proposed method against other baseline data augmentation method across various uni-modal and multi-modal setups. Our results indicate that, regardless of the dimensionality of the dataset at hand, the inclusion of a bias analysis section in the conference papers is viable. this paper is therefore a call to the community to include a bias analysis section in ACII conference submissions, similar to the ablation studies conducted in papers submitted to major machine learning conferences.

关键词： dyadic interaction mental well-being small dataset bias data augmentation fairness

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Prompt Tuning Empowering Downstream Tasks in Multimodal Federated learning 11

Prompt Tuning Empowering Downstream Tasks in Multimodal Fede...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Bao, Yichen School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331531904

Federated learning (FL) has made it possible to train global models using decentralized data in a privacy-preserving manner through the aggregation of model updates. Current FL methods have been expanded to incorporate multi-modal data for training a global model across multiple data modalities. However, global model performs poorly on downstream tasks. Recently, Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). this approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Since prompt engineering is only applied to pre-trained language models and has not been widely used in other domains, in this paper, we propose a framework to apply prompt engineering from language models to multi-modal federated learning. We design prompts for representative downstream tasks including image classification task, text classification task, and multimodal retrieval task. then, we freeze the backbone of the global model and do a few-shot sample study on prompts. Afterwards, we empirically analyze our framework via extensive experiments, and show its superiority in performance. © 2024 IEEE.

关键词： Federated learning

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A Supervised Clustering Approach to Detect Similar Soccer Players 25th

A Supervised Clustering Approach to Detect Similar Soccer Pl...

引用

25th international conference on intelligent data engineering and automated learning

作者： Vidal, Andreu Simo Sanchez-Anguix, Victor Alberola, Juan M. Valencian Res Inst Artificial Intelligence Valencia Spain Univ Politecn Valencia Inst Tecnol Informat Cami Vera S-N Valencia 46022 Spain Univ Politecn Valencia Cami Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

One promising application of data analytics in soccer is the identification of groups of players with similar playing styles. this can enhance recruitment processes and assist in tactical decisions, such as substitutions during a match. Previous research has explored various methods to measure a player's contribution, identify key variables for player positions, and predict market value using machine learning techniques. However, these efforts often focus on traditional player positions or a limited set of gameplay characteristics, lacking a comprehensive analysis of player participation. this paper introduces a novel approach to clustering similar soccer players using supervised clustering and dimensionality reduction. Unlike previous studies, our method aims to refine the well-known positions of players and incorporates a wide range of in-game metrics to provide a holistic view of each player's playing style.

关键词： big data machine learning dimensionality reduction sports analytics clustering optimisation

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Design of Distance Online learning System Based on intelligent Information Archives 11th

Design of Distance Online Learning System Based on Intellige...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Zhang, Linjun Chengdu Nuesoft University Sichuan Dujiangyan China

the existing online learning system can hardly provide help to students, and students are not interested in it, so that they have no enthusiasm in learning. In this study, a remote online learning system based on intelligent information archives is designed. the system uses intelligent analysis and algorithms, combines students' learning data and personal information, and provides personalized learning recommendations and customized learning support for students. the system is also equipped with intelligent tutoring and feedback functions to provide real-time tutoring guidance and targeted feedback according to students' learning progress and performance. the experimental results show that the stability of the system in this paper is between 95% and 100% in the stability test, which is very high, and can mobilize the enthusiasm of students, so that students can learn on their own initiative. the distance online learning system based on intelligent information archives can effectively improve students' learning results. © 2024 the Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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An intelligent Approach to Determine Banana Ripeness Stages using Deep learning Models 18

An Intelligent Approach to Determine Banana Ripeness Stages ...

引用

18th INDIAcom;11th international conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Pushpa, B.R. Chirag, D.L. Bhat, Satvik Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Mysuru India

ISBN: (纸本)9789380544519

Fruit ripeness classification is important in agriculture, food processing industry, supermarkets, Ayurveda, and many more. the manual identification is tedious, time-consuming, and laborious. Hence, deep learning techniques have gained popularity recently as a computer vision method for automatically classifying fruit ripeness simplifies feature engineering work. Differentiating between the visual characteristics of fruits at different stages of ripeness is a difficult task for machine learning systems. In this proposed work, two CNN models such as InceptionV3 and VGG19 are used to invent the best deep-learning model to categorize bananas into their three distinct ripeness stages. the experiments are carried out using self-created datasets consisting of 687 image samples of three different stages. the first dataset consists of individual banana image samples and the second dataset contains banana bunch sample images. the CNN models are trained and tested in the range of 80:20. It is observed that VGG19 achieves better accuracy than InceptionV3 in predicting three ripening stages of banana fruit with an accuracy of 98.1% and 97.5%. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Banana fruit Classification Deep learning Ripeness stages Transfer learning

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CMIB: A Cross Modal Interaction with Information Bottleneck Framework for Multimodal Survival Analysis 11

CMIB: A Cross Modal Interaction with Information Bottleneck ...

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11th IEEE international conference on Cybernetics and intelligent Systems (CIS) / 11th IEEE international conference on Robotics, Automation and Mechatronics (RAM)

作者： Lai Zhouyou Long Lifan Cui Jiaqi Xiong Deng Liu Yuanjun Yin Hao Wang Yan Sichuan Univ Sch Software Chengdu Peoples R China Sichuan Univ Sch Comp Sci Chengdu Peoples R China Suining Cent Hosp Dept Hepatobiliary Surg Suining Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200;9798350364194

Recently, multimodal survival analysis that integrates histology images and genomic data has become a hot topic. Existing multimodal survival analysis methods have evolved from direct fusion strategies to cross-modal attention mechanisms to incorporate multimodal features. However, these methods ignore the redundancy and noise in the fusion features. To solve this problem, we introduced a Cross Modal Interaction with Information Bottleneck (CMIB) framework for multimodal survival analysis, which filters out redundancy and noise while exploring the latent complementary information across modalities. Specifically, CMIB uses the Private Feature Extraction Block (PFEB) and Common Feature Extraction Block (CFEB) to extract the private and the common features of different modalities, respectively. Subsequently, it captures the deep interactions between these features through Co-Attention (CA). Additionally, a Multimodal Information Bottleneck (MIB) is employed to yield a robust representation of the fused features. To verify the effectiveness of CMIB, we conducted extensive experiments on three public TCGA datasets. the results show that CMIB outperforms the current state-of-the-art methods.

关键词： Survival Analysis Computational pathology Multimodal learning Information Bottleneck

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Multimodal Sentiments: Unraveling Text and Emoji Dynamics through Deep learning 11

Multimodal Sentiments: Unraveling Text and Emoji Dynamics Th...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Pratibha Kaur, Amandeep Khurana, Meenu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chitkara University School of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350350357

the growth of the internet has made it possible for people to use text messaging programs to communicate and share their thoughts about everyday activities as well as regional and national events. this overview examines how text mining is developing, with a focus on sentiment analysis and the newly-emerging discipline of emotion detection. It primarily focuses on the use of deep learning for code-mixed or Hinglish languages in sentiment analysis and emotion identification. this research looks at study designs, state-of-the-art models at the moment, and how well deep learning interprets data with mixed text and emojis. Insights into modern sentiment analysis are sought by shedding light on the complex interplay between textual and visual clues, with a particular emphasis on developmental stages, obstacles, and future prospects. the study provides direction for further study and advancement in the areas of multimodal sentiment analysis and emotion recognition in Hinglish or code mixed data. To further improve our comprehension of this field, a bibliometric analysis is also carried out to pinpoint the most pertinent sources, trends, and geographic patterns. © 2024 IEEE.

关键词： Deep learning

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