频谱对比特征在语音情感识别中的影响分析

Shreyamsha Kumar, Swarnalaxmi Thiruvenkadam
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引用次数: 3

摘要

特征提取是语音情感识别的重要组成部分。一些情绪由于其特征高度相似而变得难以区分,这导致预测精度较低。本文分析了光谱对比特征对提高这类情绪识别精度的影响。本研究选择RAVDESS数据集。使用SAVEE数据集、CREMA-D数据集和JL语料库数据集测试其在不同英语口音下的性能。除此之外,EmoDB数据集还被用于研究其在德语中的表现。频谱对比特征在识别唤醒水平差异显著的情绪方面表现良好,提高了语音情绪识别系统的预测精度,并得到了详细的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Analysis of the Impact of Spectral Contrast Feature in Speech Emotion Recognition
Feature extraction is an integral part in speech emotion recognition. Some emotions become indistinguishable from others due to high resemblance in their features, which results in low prediction accuracy. This paper analyses the impact of spectral contrast feature in increasing the accuracy for such emotions. The RAVDESS dataset has been chosen for this study. The SAVEE dataset, CREMA-D dataset and JL corpus dataset were also used to test its performance over different English accents. In addition to that, EmoDB dataset has been used to study its performance in the German language. The use of spectral contrast feature has increased the prediction accuracy in speech emotion recognition systems to a good degree as it performs well in distinguishing emotions with significant differences in arousal levels, and it has been discussed in detail. 
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