Speech emotion recognition (SER) has received a great deal of attention in recent years in the context of spontaneous conversations. While there have been notable results on datasets like the well known corpus of naturalistic dyadic conversations, IEMOCAP, for both the case of categorical and dimensional emotions, there are few papers which try to predict both paradigms at the same time. Therefore, in this work, we aim to highlight the performance contribution of multi-task learning by proposing a multi-task, multi-modal system that predicts categorical and dimensional emotions. The results emphasise the importance of cross-regularisation between the two types of emotions. Our approach consists of a multi-task, multi-modal architecture that uses parallel feature refinement through self-attention for the feature of each modality. In order to fuse the features, our model introduces a set of learnable bridge tokens that merge the acoustic and linguistic features with the help of cross-attention. Our experiments for categorical emotions on 10-fold validation yield results comparable to the current state-of-the-art. In our configuration, our multi-task approach provides better results compared to learning each paradigm separately. On top of that, our best performing model achieves a high result for valence compared to the previous multi-task experiments.
KAIST（韩国科学技术院）；Upstage（Upstage公司）；Seoul National University（首尔大学）
We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover's Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD …
Université Tunis El Manar（突尼斯埃尔马纳尔大学）；Ecole Nationale d'Ingénieurs de Tunis-ENIT（突尼斯国家工程学院）
The purpose of speech emotion recognition system is to classify speakers utterances into different emotional states such as disgust, boredom, sadness, neutral and happiness. Speech features that are commonly used in speech emotion recognition rely on global utterance level prosodic features. In our work, we evaluate the impact of frame level feature extraction. The speech samples are from Berlin emotional database and the features extracted from these utterances are energy, different variant of mel frequency cepstrum coefficients, velocity and acceleration features.
There are two paradigms of emotion representation, categorical labeling and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional…
Renmin University of China（中国人民大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；National University of Singapore（新加坡国立大学）
The emotional state of a speaker can be influenced by many different factors in dialogues, such as dialogue scene, dialogue topic, and interlocutor stimulus. The currently available data resources to support such multimodal affective analysis in dialogues are however limited in scale and diversity. In this work, we propose a Multi-modal Multi-scene Multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. M3 ED is annotated with 7 emotion categories (happy, surprise, sad, disgust, anger…
Friedrich-Schiller-Universität Jena（耶拿弗里德里希·席勒大学）；Jena University Language & Information Engineering (JULIE) Lab（耶拿大学语言与信息工程实验室）
We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation format. EmoBank excels with a bi-perspectival and bi-representational design. On the one hand, we distinguish between writer's and reader's emotions, on the other hand, a subset of the corpus complements dimensional VAD annotations with categorical ones based on Basic Emotions. We find evidence for the supremacy of the reader's perspective in terms of IAA and rating intensity, and achieve close-to-human perf…
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis
Beijing Institute of Technology（北京理工大学）；The Open University（开放大学）；The Hong Kong Polytechnic University（香港理工大学）；Chongqing University（重庆大学）；China Mobile Communication Group Tianjin Co., Ltd.（中国移动通信集团天津有限公司）；Nanjing University（南京大学）；Zhengzhou University of Light Industry（郑州轻工业大学）
NaCTeM（国家文本挖掘中心）；The University of Manchester（曼彻斯特大学）；Umm Al-Qura University（乌姆·库拉大学）
A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information between emotions. ERC is also not equally dependent on all embedded features in the semantic space, which makes the high-dimensional SCL inefficient. To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Lear…
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology (HUST)（华中科技大学人工智能与自动化学院）；Institute of Artificial Intelligence, HUST（华中科技大学人工智能研究院）；Hubei Key Laboratory of Brain-inspired Intelligent Systems, HUST（华中科技大学湖北省脑启发智能系统重点实验室）；Key Laboratory of Image Processing and Intelligent Control (HUST), Ministry of Education（华中科技大学图像信息处理与智能控制教育部重点实验室）
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition
School of computer and Information Engineering, Central South University of Forestry and Technology（中南林业科技大学计算机与信息工程学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；State University of New York（纽约州立大学）
Indian Institute of Technology Patna（巴特那印度理工学院）；Nanyang Technological University（南洋理工大学）
Related tasks often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both. The multi-modal inputs (i.e., text, acoustic and visual frames) of a video convey diverse and distinctive information, and usually do not have equal contribution in the decision making. We propose a context-level inter-modal attention framework for simultaneously predicting the sentiment and expressed emotions of an utterance. We evaluate our proposed approach on CMU…
PingAn Life Insurance Company of China, Ltd.（中国平安人寿保险股份有限公司）；School of Computer Science, Beijing University of Posts and Telecommunications（北京邮电大学计算机学院）
Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems. Existing studies on ERC primarily focus on summarizing the context information in a conversation, however, ignoring the differentiated emotional behaviors within and across different modalities. Designing appropriate strategies that fit the differentiated multi-modal emotional behaviors can produce more accurate emotional predictions. Thus, we propose the DialogueTransformer to explore the differentiated emotional behaviors from the intra- and inter-modal perspectives. For intra-modal, we con…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Queen Mary University of London（伦敦玛丽女王大学）
This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal fusion method, Squeeze-and-Excitation Fusion (SEFusion), and embed it into our system for emotion classification in memes. SEFusion is a simple fusion method that employs fully connected layers, reshaping, and matrix multiplication. SEFusion learns a weight for each modality and then applies it to its own modality feature. We evaluate the performance of our system on the three Memotion 3 sub-tasks. Among all participating systems in this Memot…
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations
Central South University of Forestry and Technology（中南林业科技大学）；Changsha Medical University（长沙医学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Cross-modal Consistency Guidance for Robust Emotion Control in Auto-Regressive TTS Models
Alibaba-NTU Global e-Sustainability CorpLab（阿里-国立大学全球可持续发展公司实验室）；Nanyang Technological University（南洋理工大学）；College of Computing and Data Science（计算与数据科学学院）；Alibaba（阿里）；Alibaba Inc.（阿里公司）
Emotion recognition is a challenging task due to limited availability of in-the-wild labeled datasets. Self-supervised learning has shown improvements on tasks with limited labeled datasets in domains like speech and natural language. Models such as BERT learn to incorporate context in word embeddings, which translates to improved performance in downstream tasks like question answering. In this work, we extend self-supervised training to multi-modal applications. We learn multi-modal representations using a transformer trained on the masked language modeling task with audio, visual and text fe…
Recent works on multi-modal emotion recognition move towards end-to-end models, which can extract the task-specific features supervised by the target task compared with the two-phase pipeline. However, previous methods only model the feature interactions between the textual and either acoustic and visual modalities, ignoring capturing the feature interactions between the acoustic and visual modalities. In this paper, we propose the multi-modal end-to-end transformer (ME2ET), which can effectively model the tri-modal features interaction among the textual, acoustic, and visual modalities at the…
Multi-modal Anchor Gated Transformer with Knowledge Distillation for Emotion Recognition in Conversation
School of Computer Science and Technology, China University of Mining and Technology（计算机科学与技术学院，中国矿业大学）；Mine Digitization Engineering Research Center of Ministry of Education, China University of Mining and Technology（教育部矿山数字化工程研究中心，中国矿业大学）
Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful, as applications will be able to use specific subsets of available modalities, as per the available data. Current systems dealing with Multi-modal functionality fail to leverage and capture - the context of the conversation through all modalities, the dependency between the listener(s) and speaker emotional states, and …
CMATH: Cross-Modality Augmented Transformer with Hierarchical Variational Distillation for Multimodal Emotion Recognition in Conversation
Chongqing University of Technology（重庆理工大学）；Fuzhou University（福州大学）；China Automotive Engineering Research Institute Co., Ltd.（中国汽车工程研究院股份有限公司）；Chongqing Changan Automobile Co., Ltd.（重庆长安汽车股份有限公司）
Central South University（中南大学）；Hunan University（湖南大学）
Emotion recognition in conversation (ERC) aims to analyze the speaker's state and identify their emotion in the conversation. Recent works in ERC focus on context modeling but ignore the representation of contextual emotional tendency. In order to extract multi-modal information and the emotional tendency of the utterance effectively, we propose a new structure named Emoformer to extract multi-modal emotion vectors from different modalities and fuse them with sentence vector to be an emotion capsule. Furthermore, we design an end-to-end ERC model called EmoCaps, which extracts emotion vectors …
This paper aims to test whether a multi-modal approach for music emotion recognition (MER) performs better than a uni-modal one on high-level song features and lyrics. We use 11 song features retrieved from the Spotify API, combined lyrics features including sentiment, TF-IDF, and Anew to predict valence and arousal (Russell, 1980) scores on the Deezer Mood Detection Dataset (DMDD) (Delbouys et al., 2018) with 4 different regression models. We find that out of the 11 high-level song features, mainly 5 contribute to the performance, multi-modal features do better than audio alone when predictin…
Jadavpur University（贾达普大学）；IIT Kharagpur（印度理工学院卡哈拉格普尔分校）；Shrutinandan School of Music（Shrutinandan音乐学院）
It is already known that both auditory and visual stimulus is able to convey emotions in human mind to different extent. The strength or intensity of the emotional arousal vary depending on the type of stimulus chosen. In this study, we try to investigate the emotional arousal in a cross-modal scenario involving both auditory and visual stimulus while studying their source characteristics. A robust fractal analytic technique called Detrended Fluctuation Analysis (DFA) and its 2D analogue has been used to characterize three (3) standardized audio and video signals quantifying their scaling expo…
Beijing University of Posts and Telecommunications（北京邮电大学）；East China Normal University（华东师范大学）；Shanghai University of International Business and Economics（上海对外经贸大学）
The audio-video based multimodal emotion recognition has attracted a lot of attention due to its robust performance. Most of the existing methods focus on proposing different cross-modal fusion strategies. However, these strategies introduce redundancy in the features of different modalities without fully considering the complementary properties between modal information, and these approaches do not guarantee the non-loss of original semantic information during intra- and inter-modal interactions. In this paper, we propose a novel cross-modal fusion network based on self-attention and residual…
Seoul National University（首尔大学）；Idiap Research Institute（伊迪亚普研究所）
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by fusing the knowledge from the various modalities independently for performing emotion classification. In contrast to these approaches, we tackle the problem by introducing an attention mechanism to combine the information. In this regard, we first apply a neural network to obtain hidden representations of the modalities. Then, the attention mechanism is defined to select and aggregate important parts of …
General embeddings like word2vec, GloVe and ELMo have shown a lot of success in natural language tasks. The embeddings are typically extracted from models that are built on general tasks such as skip-gram models and natural language generation. In this paper, we extend the work from natural language understanding to multi-modal architectures that use audio, visual and textual information for machine learning tasks. The embeddings in our network are extracted using the encoder of a transformer model trained using multi-task training. We use person identification and automatic speech recognition…
Beijing University of Posts and Telecommunications（北京邮电大学）；East China Normal University（华东师范大学）；Shanghai University of International Business and Economics（上海对外经贸大学）
Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition. Existing approaches use directional pairwise attention or a message hub to fuse language, visual, and audio modalities. However, those approaches introduce information redundancy when fusing features and are inefficient without considering the complementarity of modalities. In this paper, we propose an efficient neural network to learn modality-fused representations with CB-Transformer (LMR-CBT) for multimodal emotion recognition from unaligned…
The University of Tokyo（东京大学）；Keio University（庆应义塾大学）；Huawei Technologies Japan K.K.（华为技术日本株式会社）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；Huawei Turkey R&D Center（华为土耳其研发中心）
Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 hours, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures w…
