Emotion Recognition

. In this paper, we proposed a weighted discrete K-nearest neighbor (weighted D-KNN) classification algorithm for detecting and evaluating emotion from Mandarin speech. In the experiments of the emotion recognition, …...

This paper presents an approach to feature compensation for emotion recognition from speech signals. In this approach, the intonation groups (IGs) of the input speech signals are extracted first. The …...

Recognizing the emotive meaning of text can add another dimension to the under- standing of text. We study the task of automatically categorizing sentences in a text into Ekman’s six …...

AbstractTextual information is an important communication medium contained rich expression of emotion, and emotion recognition on text has wide applications. Word emotion analysis is fundamental in the problem of textual …...

AbstractThe LTSE-VAD is one of the best known algorithms for voice activity detection. In this paper we present a modified version of this algorithm, that makes the VAD decision not …...

AbstractA monitoring system to detect emotional outbursts in day-to-day communication is presented. The anger monitor was tested in a household and in parallel in an office surrounding. Although the state …...

AbstractStandardized corpora are the foundation for spoken language research. In this work, we introduce an annotated and standardized corpus in the Spoken Dialog Systems (SDS) domain. Data from the Let's …...

In this paper, we explore the classification of emotions in songs, using the music and the lyrics representation of the songs. We intro- duce a novel corpus of music and …...

AbstractDeriving the emotion of a human speaker is a hard task, especially if only the audio stream is taken into account. While state-of-the-art approaches already provide good results, adaptive methods …...

AbstractA major challenge in the field of automatic recognition of emotion and affect in speech is the subjective nature of affect labels. The most common approach to acquiring affect labels …...

AbstractAutomated emotion recognition has a number of applications in Interactive Voice Response systems, call centers, etc. While employing existing feature sets and methods for automated emotion recognition has already achieved …...

AbstractEmotional aspects play a vital role in making human communication a rich and dynamic experience. As we introduce more automated system in our daily lives, it becomes increasingly important to …...

AbstractEmotion Recognition (ER) is an important part of dialogue analysis which can be used in order to improve the quality of Spoken Dialogue Systems (SDSs). The emotional hypothesis of the …...

In this paper, we describe our approach of en- abling an interactive dialogue system to rec- ognize user emotion and sentiment in real- time. These modules allow otherwise con- ventional …...

AbstractSentiment Analysis is a broad task that involves the analysis of various aspect of the natural language text. However, most of the approaches in the state of the art usually …...

This paper tried to amplify a sense of emotion toward drama. Using Long Short-Term Memory Recurrent Neural Network to model and predict dynamic emotion(Arousal and Valence) recognition. After building model, …...

Modern research on emotion recognition often deals with time-continuously labelled spontaneous interactions. Such data is much closer to real world problems in contrast to utterance-level categorical labelling in acted emotion …...

AbstractEmotion recognition in conversations is crucial for the development of empathetic machines. Present methods mostly ignore the role of inter-speaker dependency relations while classifying emotions in conversations. In this paper, …...

AbstractNowadays, social media have become a platform where people can easily express their opinions and emotions about any topic such as politics, movies, music, electronic products and many others. On …...

AbstractNatural human communication is nuanced and inherently multi-modal. Humans possess specialised sensoria for processing vocal, visual, and linguistic, and para-linguistic information, but form an intricately fused percept of the multi-modal …...

AbstractWith text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of …...

AbstractWe proposed a model to address emotion recognition in textual conversation based on using automatically extracted features and human engineered features. The proposed model utilizes a fast gated-recurrent-unit backed by …...

AbstractEmotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more …...

AbstractWhen reading “I don’t want to talk to you any more”, we might interpret this as either an angry or a sad emotion in the absence of context. Often, the …...

AbstractStudies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings where both modalities …...

AbstractEmotion recognition in dialogue systems has gained attention in the field of natural language processing recent years, because it can be applied in opinion mining from public conversational data on …...

AbstractIn this paper, we address the task of utterance level emotion recognition in conversations using commonsense knowledge. We propose COSMIC, a new framework that incorporates different elements of commonsense such …...

AbstractThe recognition of emotion and dialogue acts enriches conversational analysis and help to build natural dialogue systems. Emotion interpretation makes us understand feelings and dialogue acts reflect the intentions and …...

AbstractA fundamental essence for emotional speech analysis towards emotion recognition is a good database. Database collected from natural scenarios consists of spontaneous emotions, but there are several issues in collection …...

AbstractThe human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on …...

AbstractThe task of Dialogue Act Classification (DAC) that purports to capture communicative intent has been studied extensively. But these studies limit themselves to text. Non-verbal features (change of tone, facial …...

AbstractImposing the style of one image onto another is called style transfer. For example, the style of a Van Gogh painting might be imposed on a photograph to yield an …...

AbstractEffective fusion of data from multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data. In this paper, we propose adaptive fusion …...

Detecting what emotions are expressed in text is a well-studied problem in natural lan- guage processing. However, research on finer- grained emotion analysis such as what causes an emotion is …...

AbstractExisting works in multimodal affective computing tasks, such as emotion recognition and personality recognition, generally adopt a two-phase pipeline by first extracting feature representations for each single modality with hand …...

AbstractEmotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and …...

AbstractDue to the popularity of intelligent dialogue assistant services, speech emotion recognition has become more and more important. In the communication between humans and machines, emotion recognition and emotion analysis …...

AbstractWith the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research …...

AbstractFor research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make …...

AbstractThe long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems. Such systems should have the ability to develop an emotional connection with the users, consequently, emotion …...

AbstractThe ability to recognise emotions lends a conversational artificial intelligence a human touch. While emotions in chit-chat dialogues have received substantial attention, emotions in task-oriented dialogues remain largely unaddressed. This …...

AbstractAs an emerging research topic in natural language processing community, emotion recognition in multi-party conversations has attained increasing interest. Previous approaches that focus either on dyadic or multi-party scenarios exert …...

AbstractInformation integration from different modalities is an active area of research. Human beings and, in general, biological neural systems are quite adept at using a multitude of signals from different …...

AbstractThe ability to recognize emotions in conversations is necessary and important for the online chatbot to do tasks such as empathetic response generation and emotional support. Present researches mainly focus …...