情绪识别研究论文

Sayali Barsagade, Sakshi Moon, Dhyaneshwari Itnakr, Damini Asoda, Vaibhav Wankhede, Dr. Dhananjay Dumbere
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引用次数: 0

摘要

作为一种公认的机器学习算法,在不同的技术领域已经完成了大量的研究,其中语音对机器领域的研究产生了重大影响,尤其是在情感计算领域。在现实世界中,语音的潜力、算法的进步和应用都在不断增加。人类语音包含准语言信息,可以使用不同的量化特征(如音高、强度)来表示其三角结果。它通常通过三个关键步骤来实现:数据处理、特征提取和基于基本情感特征的分类。这些步骤的性质有助于利用人类语音的不同特征,通过使用多语言方法获得准确的结果。许多技术已被用于从信号中提取情感,包括许多成熟的语音分析和分类技术。信号是人机交互(HCI)中一个重要但具有挑战性的组成部分,在计算机机器的机器学习方面,通过各种不同的视角和给定的信号,人机交互(HCI)是一个重要但具有挑战性的组成部分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research Paper on Emotion Recognition
As a recognizing in machine learning algorithm a significant amount of in different various field has been done in many technologies field of machines through which speech has a major impact research interest, especially in the affective computing domain. Increasing potential, algorithmic advancements, and applications in real-world. This human speech contains para-linguistic information that can be represented using different various quantitative features such as pitch, intensity for its deltaic result. It is commonly achieved following three key steps: data processing, feature extraction, and classification based on the underlying emotional features. The nature of these steps, help with the distinct features of human speech, to get the exact result through the underpin with the use of ML methods. Many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Emotion recognition the review covers databases used, emotions extracted, contributions made toward emotion recognition and limitations related to it. signals are an important but challenging component of Human-Computer Interaction (HCI) in machine learning aspect in computer machines through various different perspective and given signals
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