Using Vocals Determine Human Emotion
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About this ebook
The human voice is very versatile and carries a multitude of emotions. Emotion in speech carries extra insight about human actions. Through further analysis, we can better understand the motives of people, whether they are unhappy customers or cheering fans. Humans are easily able to determine the emotion of a speaker, but the field of emotion recognition through machine learning is an open research area. We begin our study of emotion in speech by detecting one emotion. Specifically, we investigate the classification of happy, sad, anger or other related emotions in speech samples. In our analysis of emotion, we start by delineating the data used. We transition to discussing our methodology, and through this analysis, we investigate the best algorithms to select features that are relevant to predicting emotion. We also consider multiple machine learning models with which to classify emotion.
Faiz ul haque Zeya
Faiz ul haque Zeya(1975-) did his MS from University of Tulsa, OK USA in Computer science AI.Started working on AI in BE and then took courses in AI in MS.He teaches AI for 7 years in different universities in Pakistan.His citation which is the work of BE project in book "Agents and computational autonomy" is present in around 400 libraries worldwide. He is CEO of Transys which develops software agents.
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Using Vocals Determine Human Emotion - Faiz ul haque Zeya
1.1 Project Overview
Vocal emotion recognition object is to consequently recognize the emotional or physical condition of an individual from his/her voice. The emotional condition of an individual hidden in his conversation is a significant factor of human communication and cooperation as it gives input in correspondence while not modifying linguistic contents. The standard procedure for discourse feeling acknowledgment comprises of three parts: Signal Processing, extraction of features and classification of features. The processing of signal applies acoustic channel on unique sound signals and parts it into important units. The extraction of feature is the delicate point in discourse feeling acknowledgment because features need to both proficiently portray the emotional substance of a human speech and not rely upon the verbal substance or even the speaker. At long last, emotion classification will map the matrix of the features to the labelled emotions. We are determining human emotions through vocals in Multi-language (English and Urdu). This can be used in various industries and niche like Interviews, Dealing with customers, finding the psychological behavior of a person and can be integrated with the personal assistant(Alexa, Cortana) to suggest songs depending on the mood and emotions of the person.
1.2 Project Deliverables
The Deliverables of our project are listed below: