Feature representation plays a critical role in speech emotion recognition (SER). As a method of data dimensionality reduction, Non-negative Matrix Factorization (NMF) can obtain the low-dimensional representation of data by matrix decomposition, and make the data more distinguishable. In order to improve the recognition ability of NMF for SER, we conduct a potential study on NMF and propose a supervised NMF model, called joint discrimination ability and similarity constraint of NMF (DSNMF). This model incorporates the discriminative information and similarity information of samples into basic NMF as prior knowledge, so that the original data can be decomposed into more distinguished low-dimensional data. Specifically, on the one hand, the labels of the training set are used to improve the discriminative ability of the model; on the other hand, with the similarity of the training samples, the data of similar samples are more highly aggregated in the low-dimensional space. In addition, the convergence of DSNMF is proved theoretically and experimentally. Extensive experiments on EMODB and IEMOCAP corpora show that the proposed approach has a better classification effect on low-dimensional representation data than other NMF models.

特征表示在语音情感识别（SER）中起着至关重要的作用。作为数据降维的一种方法，非负矩阵分解（NMF）可以通过矩阵分解获得数据的低维表示，并使数据更具可分辨性。为了提高NMF对SER的识别能力，我们对NMF进行了潜在的研究，并提出了一种受监督的NMF模型，称为联合判别能力和NMF的相似性约束（DSNMF）。该模型将样本的区分性信息和相似性信息作为先验知识合并到基本NMF中，以便可以将原始数据分解为更出色的低维数据。具体来说，一方面，训练集的标签用于提高模型的判别能力；另一方面 另一方面，由于训练样本的相似性，相似样本的数据在低维空间中的聚合度更高。另外，从理论和实验上证明了DSNMF的收敛性。在EMODB和IEMOCAP语料库上的大量实验表明，与其他NMF模型相比，该方法对低维表示数据具有更好的分类效果。