深入分析语音产生,听觉系统,情绪理论和情绪识别

Yeşím Ülgen Sönmez, A. Varol
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引用次数: 4

摘要

人工智能和机器学习被用来使机器更智能。从语音信号中识别SER情绪是人工智能方法研究的一个机器学习难题。由于语音信号包含不同的频率和特征,因此难以分析。通过信号处理方法对语音进行数字化处理,通过声学分析获得声音特征。这些特征随着情绪的变化而变化,比如悲伤、恐惧、愤怒、快乐、无聊和惊讶。通过对语音产生系统和人类听觉系统的特征进行建模,提出了分析方法。本研究从语音产生系统、听觉系统、情绪产生系统、情绪定义与理论、情绪识别模型等方面进行了研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
In-Depth Analysis of Speech Production, Auditory System, Emotion Theories and Emotion Recognition
Artificial intelligence and machine learning are used to make machines more intelligent. SER emotion recognition from speech signals is a difficult problem for machine learning which artificial intelligence method is. The speech signal is difficult to analyze because it contains different frequencies and features. Speech is digitized by signal processing methods and sound characteristics are obtained by acoustic analysis. These features vary by emotions such as sadness, fear, anger, happiness, boredom and surprise. Analysis methods emerged by modeling the features of speech production system and human hearing system. In this study, speech production system, hearing system, emotion production system, emotion definition and theories, emotion recognition models are examined.
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