Human Emotion Identification from Speech using Neural Network

Bhoomi Rajdeep, H. Patel, S. Iyer
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Abstract

Detection of mood and behavior by voice analysis which helps to detect the speaker’s mood by the voice frequency. Here, I aim to present the mood like happy, and sad and behavior detection devices using machine learning and artificial intelligence which can be detected by voice analysis. Using this device, it detects the user’s mood. Moreover, this device detects the frequency by trained model and algorithm. The algorithm is well trained to catch the frequency where it helps to identify the mood happy or sad of the speaker and behavior. On the other hand, behavior can be predicted in form, it can be either positive or negative. So, this device helps to prevent mental health issues and is used in medical and gaming testing. Furthermore, it is easy to identify a person’s mood by their expression and by their actions in daily activities. But it is effective and challenging to detect mood and behavior by voice frequency because a rich environment affects the most. Thus, this device works as a signal processing.
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基于神经网络的语音情感识别
通过声音分析来检测说话人的情绪和行为,通过声音频率来检测说话人的情绪。在这里,我的目标是用机器学习和人工智能来呈现快乐、悲伤等情绪和行为检测设备,这些设备可以通过语音分析来检测。使用这个设备,它可以检测用户的情绪。此外,该装置通过训练好的模型和算法进行频率检测。该算法经过良好的训练,可以捕捉频率,从而帮助识别说话者的情绪和行为是高兴还是悲伤。另一方面,行为可以通过形式来预测,它可以是积极的也可以是消极的。因此,这个设备有助于预防心理健康问题,并用于医疗和游戏测试。此外,通过一个人在日常活动中的表情和行为,很容易判断出他的情绪。但是,通过声音频率来检测情绪和行为是有效的,也是具有挑战性的,因为丰富的环境影响最大。因此,该装置作为信号处理。
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