In this paper, we analyse segmented speech phonemes with Convolutional filters, after embedding them in Reconstructed Phase Space (RPS). These feature extracting Convolutional filters are trained on the embedded speech data from scratch and are also fine-tuned from networks trained with other data. Reconstruction of Phase Space portrays the dynamics of an observed system as a geometric representation. We present a study highlighting the discriminative capacity of the features extracted through Convolutional Neural Network (CNN) from the textural pattern and shape of this geometric representation. CNNs are heavily used in image-related tasks, but have not seen application on phase space portraits, possibly due to the higher dimensionality of the embedding. However, we find that the application of CNN on restricted bi-dimensional RPS, characterizes the space well than prior methods on high dimensional embeddings. We show experimental results supporting the use of RPS with CNN (RPS-CNN) for phoneme classification. The results affirm that essential signal characteristics are automatically quantified from the phase portraits of speech and can be used in place of conventional techniques involving frequency domain transformations.

在本文中，我们将分段语音音素嵌入到重构相空间（RPS）中，然后使用卷积滤波器对其进行分析。这些特征提取卷积滤波器从头开始对嵌入的语音数据进行训练，并且也从使用其他数据训练的网络中进行了微调。相空间的重构将观测到的系统的动力学描绘为几何表示。我们提出了一项研究，突出了通过卷积神经网络（CNN）从该几何表示的纹理图案和形状中提取的特征的判别能力。CNN在与图像相关的任务中被大量使用，但由于嵌入的尺寸较大，因此尚未在相空间肖像上得到应用。但是，我们发现CNN在受限的二维RPS上的应用，与高维嵌入中的现有方法相比，它具有更好的空间特征。我们显示了支持RPS与CNN（RPS-CNN）进行音素分类的实验结果。结果证实，基本信号特征是从语音的相像中自动量化的，可以代替涉及频域变换的常规技术使用。