Abstract Homo-sapiens suffer from psychogenic pain due to current day lifestyle. According to psychologists, stress is the most destructive form of psychalgia and it is a vicious companion for this species. Immoderate levels of stress may lead to the death of many individuals. Normally, the presence of stress gives rise to certain emotions which can be detected to predict stress levels of a person. This paper proposes the development of mechanized and efficient Speech Emotion Recognition (SER) for stress level analysis. The paper investigates the performance of perceptual based speech features like Revised Perceptual Linear Prediction Coefficients, Bark Frequency Cepstral Coefficients, Perceptual Linear Predictive Cepstrum, Gammatone Frequency Cepstral coefficient, Mel Frequency Cepstral Coefficient, Gammatone Wavelet Cepstral Coefficient and Inverted Mel Frequency Cepstral Coefficients on SER. The novelty of this work involves application of a SemiEager (SemiE) learning algorithm for evaluating auditory cues. SemiE offers advantages over eager and lazy based learning by reducing the computational cost. Stress level recognition being the main objective, the Speech Under Simulated and Actual Stress (SUSAS) benchmark database is used for performance analysis. A comparative analysis is presented to demonstrate the improvement in the SED performance. An overall accuracy of 90.66% recognition of stress related emotions is achieved.

中文翻译：

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使用半渴望学习的语音压力识别

摘要 现代人因现代生活方式而遭受心因性疼痛。根据心理学家的说法，压力是最具破坏性的精神痛形式，它是这个物种的恶毒伴侣。过度的压力可能导致许多人死亡。通常，压力的存在会引起某些情绪，可以检测到这些情绪来预测一个人的压力水平。本文提出了开发用于压力水平分析的机械化和高效的语音情感识别 (SER)。该论文研究了基于感知的语音特征的性能，如修正的感知线性预测系数、巴克频率倒谱系数、感知线性预测倒谱、伽马通频率倒谱系数、梅尔频率倒谱系数、Gammatone 小波倒谱系数和 SER 上的倒置梅尔频率倒谱系数。这项工作的新颖之处在于应用 SemiEager (SemiE) 学习算法来评估听觉线索。通过降低计算成本，SemiE 比基于急切和懒惰的学习更具优势。压力水平识别是主要目标，模拟和实际压力下的语音（SUSAS）基准数据库用于性能分析。提供了比较分析以证明 SED 性能的改进。实现了 90.66% 的压力相关情绪识别的整体准确率。通过降低计算成本，SemiE 比基于急切和懒惰的学习更具优势。压力水平识别是主要目标，模拟和实际压力下的语音（SUSAS）基准数据库用于性能分析。提供了比较分析以证明 SED 性能的改进。实现了 90.66% 的压力相关情绪识别的整体准确率。通过降低计算成本，SemiE 比基于急切和惰性的学习更具优势。压力水平识别是主要目标，模拟和实际压力下的语音（SUSAS）基准数据库用于性能分析。提供了比较分析以证明 SED 性能的改进。实现了 90.66% 的压力相关情绪识别的整体准确率。