中文摘要：

为解决语音情感识别分类器空间复杂度高的问题,将语义细胞应用于语音情感识别领域.以语义细胞混合模型为核心,提出基于单层语义细胞的语音情感识别（IC-S）算法以及基于说话人-情感识别的双层语义细胞识别（IC-D）算法.在CASIA（汉语）和SAVEE（英语）情感语料库中进行交叉验证实验,并利用F值评判识别性能.结果表明：相比常用算法（如：SVM）,IC-S算法在空间和时间复杂度上具有优势;IC-D算法与SVM算法识别准确率相似,可以有效降低模型存储空间的复杂度,适用于说话人分类较少或较为固定的场景.

英文摘要：

Information cell was applied in the field of speech emotion recognition to address the problem of high space complexity of speech emotion recognition classifier. Single-layered information cell （IC-S） algorithm and speaker-emotion recognition based dual-layered information cell （IC-D） algorithm were proposed in the light of information cell mixture model. Cross-validation test on CASIA （in Chinese） and SAVEE （in English） corpus were conducted using F-score as the indicator of recognition performance. Results show that the IC-S algorithm has advantages in both time and space complexity compared to common algorithms like SVM. IC-D algorithm achieves similar recognition performance as SVM. IC-D algorithm can reduce the space complexity significantly and it is suitable for scenarios with few or fixed speakers.