孟加拉语语音情绪预测系统

Prashengit Dhar, Sunanda Guha
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引用次数: 5

摘要

从言语中预测人类情绪是当今重要的研究课题。一个人的精神状态可以通过情感来理解。本文拟开展的研究工作是基于人类语言的情感识别。该系统在识别说话人的情绪方面发挥了重要作用。它对智能家居环境有很大的用处。一个人可以理解在家或在其他地方的人的情绪。大学、服务中心或医院可以利用该情绪预测系统获得有价值的决策支持系统。从音频样本信号中提取出- mfcc (Mel-Frequency倒谱系数)和LPC等特征。音频是通过录制演讲来收集的。将自己收集的数据集和流行的Ravdees数据集相结合进行了测试。自收集数据集命名为ABEG。本研究使用MFCC和LPC特征对情绪预测进行训练和测试。本研究分为愤怒情绪班、快乐情绪班和中性情绪班。本文应用了不同的机器学习算法,并对结果进行了比较。与其他机器学习算法相比,逻辑回归算法表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A System to Predict Emotion from Bengali Speech
: Predicting human emotion from speech is now important research topic. One’s mental state can be understood by emotion. The proposed research work is emotion recognition from human speech. Proposed system plays significant role in recognizing emotion while someone is talking. It has a great use for smart home environment. One can understand the emotion of other who is in home or may be in other place. University, service center or hospital can get a valuable decision support system with this emotion prediction system. Features like-MFCC (Mel-Frequency Cepstral Coefficients) and LPC are extracted from audio sample signal. Audios are collected by recording speeches. A test also applied by combining self-collected dataset and popular Ravdees dataset. Self-collected dataset is named as ABEG. MFCC and LPC features are used in this study to train and test for predicting emotion. This study is made on angry, happy and neutral emotion classes. Different machine learning algorithms are applied here and result is compared with each other. Logistic regression performs well as compared to other ML algorithm.
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