Speech emotional content estimation is still a challenge for building robust human–machine interaction systems. Accuracy of emotion estimation depends upon the corpus used for training and the acoustic features employed for modelling the speech signal. Generally, emotion estimation is computationally expensive, and hence, there is a need of developing alternative techniques. In this paper, a low complexity fractal-based technique has been explored. Our hypothesis is that fractal analysis would provide better emotional content estimation because of the nonlinear nature of the speech signals. Fractal analysis involves two important parameters, i.e. fractal dimension and loop area. Fractal dimension has been computed using the Katz algorithm. The investigations using a GMM-based model show that the proposed technique is capable of identifying the emotional content within the given speech signals reliably and accurately. Further, the technique is robust in the sense that it can bear the noise level in the signal up to 10 dB. The analysis also shows that the technique is gender insensitive. The scope of the investigations presented here is limited to phonemic-level analysis, although the technique works efficiently with speech phrases as well.

语音情感内容估计对于构建强大的人机交互系统仍然是一个挑战。情感估计的准确性取决于用于训练的语料库和用于对语音信号建模的声学特征。通常，情绪估计在计算上是昂贵的，因此，需要开发替代技术。本文研究了一种基于低复杂度分形的技术。我们的假设是，由于语音信号的非线性特性，分形分析将提供更好的情感内容估计。分形分析涉及两个重要参数，即分形维数和环路面积。分形维数已使用Katz算法进行了计算。使用基于GMM的模型进行的研究表明，所提出的技术能够可靠，准确地识别给定语音信号中的情感内容。此外，该技术在可以承受信号中高达10 dB的噪声水平的意义上是可靠的。分析还表明，该技术对性别不敏感。尽管该技术还可以有效地处理语音短语，但此处介绍的研究范围仅限于音位级分析。