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首页> 外文期刊>Journal of information and optimization sciences >Emotion recognition of audio/speech data using deep learning approaches
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Emotion recognition of audio/speech data using deep learning approaches

机译：使用深度学习方法对音频/语音数据的情感认识

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摘要

Speech has been the most prominent and intelligent mode of sound. It is an effective medium to communicate the emotions and attitude in a particular language. Researchers have been extensively using speech to understand the emotions of a person. Contemplating emotions by intonation and pitch of voice and relative loudness can be used for enhancing human computer interaction. In this work, we have surveyed feature extraction techniques based on prosodic features or spectral feature extraction such as MFCC, LPCC, LPC, etc. and found out that MFCC extracts the best features for recognition of emotional content. This research work utilizes some of the best existing classification techniques for recognizing human emotions and conducts a detailed comparative analysis based on statistical and mathematical results. Finally, this paper proposes an optimal model DSCNN for raw spectrogram that resulted in an un-weighted accuracy of 61% for raw spectrogram (with noise) and 79% for clean spectrogram (without noise) for the enhancement of the human emotion evaluation system.
机译：演讲是最突出和最聪明的声音模式。它是一种有效的媒介，用于传达特定语言的情绪和态度。研究人员已经广泛使用言语来了解一个人的情绪。通过语调和语音和音调音高和相对响度的调节情绪可用于提高人力计算机互动。在这项工作中，我们通过MFCC，LPCC，LPC等韵律特征或光谱特征提取等进行了调查的特征提取技术，并发现MFCC提取了识别情绪内容的最佳特征。该研究工作利用一些用于识别人类情绪的最佳现有分类技术，并根据统计和数学结果进行详细的比较分析。最后，本文提出了用于原始谱图的最佳模型DSCNN，其原始谱图（具有噪声）的未加权精度为61％，对于人类情感评估系统的增强，对清洁谱图（没有噪声）的79％。

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