Smart Multi-Model Emotion Recognition System with Deep learning

Emotion recognition is added a new dimension to the sentiment analysis. This paper presents a multi-modal human emotion recognition web application by considering of three traits includes speech, text, facial expressions, to extract and analyze emotions of people who are giving interviews. Now a days there is a rapid development of Machine Learning, Artificial Intelligence and deep learning, this emotion recognition is getting more attention from researchers. These machines are said to be intelligent only if they are able to do human recognition or sentiment analysis. Emotion recognition helps in spam call detection, blackmailing calls, customer services, lie detectors, audience engagement, suspicious behavior. In this paper focus on facial expression analysis is carried out by using deep learning approaches with speech signals and input text

Advances in Emotion Recognition: Link to Depressive Disorder

Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social text on social network platform. Recognition of emotion pattern based on explicit and implicit features extracted through wearable and other devices could be decoded through computational modeling. Meanwhile, emotion recognition and computation are critical to detection and diagnosis of potential patients of mood disorder. The chapter aims to summarize the main findings in the area of affective recognition and its applications in major depressive disorder (MDD), which have made rapid progress in the last decade

Domain-specific lexicon generation for emotion detection from text.

Emotions play a key role in effective and successful human communication. Text is popularly used on the internet and social media websites to express and share emotions, feelings and sentiments. However useful applications and services built to understand emotions from text are limited in effectiveness due to reliance on general purpose emotion lexicons that have static vocabulary and sentiment lexicons that can only interpret emotions coarsely. Thus emotion detection from text calls for methods and knowledge resources that can deal with challenges such as dynamic and informal vocabulary, domain-level variations in emotional expressions and other linguistic nuances. In this thesis we demonstrate how labelled (e.g. blogs, news headlines) and weakly-labelled (e.g. tweets) emotional documents can be harnessed to learn word-emotion lexicons that can account for dynamic and domain-specific emotional vocabulary. We model the characteristics of realworld emotional documents to propose a generative mixture model, which iteratively estimates the language models that best describe the emotional documents using expectation maximization (EM). The proposed mixture model has the ability to model both emotionally charged words and emotion-neutral words. We then generate a word-emotion lexicon using the mixture model to quantify word-emotion associations in the form of a probability vectors. Secondly we introduce novel feature extraction methods to utilize the emotion rich knowledge being captured by our word-emotion lexicon. The extracted features are used to classify text into emotion classes using machine learning. Further we also propose hybrid text representations for emotion classification that use the knowledge of lexicon based features in conjunction with other representations such as n-grams, part-of-speech and sentiment information. Thirdly we propose two different methods which jointly use an emotion-labelled corpus of tweets and emotion-sentiment mapping proposed in psychology to learn word-level numerical quantification of sentiment strengths over a positive to negative spectrum. Finally we evaluate all the proposed methods in this thesis through a variety of emotion detection and sentiment analysis tasks on benchmark data sets covering domains from blogs to news articles to tweets and incident reports

Emotion Detection for Social Robots Based on NLP Transformers and an Emotion Ontology

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums; thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement

SPA: Web-based platform for easy access to speech processing modules

This paper presents SPA, a web-based Speech Analytics platform that integrates several speech processing modules and that makes it possible to use them through the web. It was developed with the aim of facilitating the usage of the modules, without the need to know about software dependencies and specific configurations. Apart from being accessed by a web-browser, the platform also provides a REST API for easy integration with other applications. The platform is flexible, scalable, provides authentication for access restrictions, and was developed taking into consideration the time and effort of providing new services. The platform is still being improved, but it already integrates a considerable number of audio and text processing modules, including: Automatic transcription, speech disfluency classification, emotion detection, dialog act recognition, age and gender classification, non-nativeness detection, hyperarticulation detection, dialog act recognition, and two external modules for feature extraction and DTMF detection. This paper describes the SPA architecture, presents the already integrated modules, and provides a detailed description for the ones most recently integrated.info:eu-repo/semantics/publishedVersio

ASLP-MULAN: Audio speech and language processing for multimedia analytics

Our intention is generating the right mixture of audio, speech and language technologies with big data ones. Some audio, speech and language automatic technologies are available or gaining enough degree of maturity as to be able to help to this objective: automatic speech transcription, query by spoken example, spoken information retrieval, natural language processing, unstructured multimedia contents transcription and description, multimedia files summarization, spoken emotion detection and sentiment analysis, speech and text understanding, etc. They seem to be worthwhile to be joined and put at work on automatically captured data streams coming from several sources of information like YouTube, Facebook, Twitter, online newspapers, web search engines, etc. to automatically generate reports that include both scientific based scores and subjective but relevant summarized statements on the tendency analysis and the perceived satisfaction of a product, a company or another entity by the general population