基于相对韵律特征的情感分析

Harika Abburi, K. R. Alluri, A. Vuppala, Manish Shrivastava, S. Gangashetty
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引用次数: 0

摘要

最近数字媒体使用的改善使得人们通过音频来分享他们对特定实体的看法。本文提出了一种基于相对韵律特征的在线语音评论情感检测方法。现有的基于音频的情感分析系统大多使用传统的音频特征,但它们不是提取情感的问题特定特征。在这项工作中,从音频信号的正常和重读区域提取相对韵律特征来检测情感。用激励强度来确定受力区域。使用支持向量机(SVM)和高斯混合模型(GMM)分类器构建情感模型。本研究采用mod数据库。实验结果表明,相对韵律特征比韵律特征和Mel频率倒谱系数(MFCC)更能提高情感检测率,因为相对韵律特征比韵律特征更能识别情感。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment analysis using relative prosody features
Recent improvement in usage of digital media has led people to share their opinions about specific entity through audio. In this paper, an approach to detect the sentiment of an online spoken reviews based on relative prosody features is presented. Most of the existing systems for audio based sentiment analysis use conventional audio features, but they are not problem specific features to extract the sentiment. In this work, relative prosody features are extracted from normal and stressed regions of audio signal to detect the sentiment. Stressed regions are identified using the strength of excitation. Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) classifiers are used to build the sentiment models. MOUD database is used for the proposed study. Experimental results show that, the rate of detecting the sentiment is improved with relative prosody features compared with the prosody and Mel Frequency Cepstral Coefficients (MFCC) because the relative prosody features has more sentiment specific discrimination compared to prosody features.
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