自闭症干预中无监督自编码的语音情感识别

语音情感识别是人机交互中重要的研究内容，儿童自闭症干预治疗中的语音情感识别系统有助于自闭症儿童的康复，但是由于目前语音信号中的情感特征多而杂，特征提取本身就是一项具有挑战性的工作，这样不利于整个系统的识别性能.针对这一问题，提出了一种语音情感特征提取算法，利用无监督自编码网络自动学习语音信号中的情感特征，通过构建一个3层的自编码网络提取语音情感特征，把多层编码网络学习完的高层特征作为极限学习机分类器的输入进行分类，其识别率为84.14%，比传统的基于提取人为定义特征的识别方法有所提高.

A Speech Emotion Recognition Based on Unsupervised Autoencoder in the Intervention of Autism

Speech emotion recognition is an important research area in human computer interaction (HCI). The speech emotion recognition system used in the intervention therapy for autistic children is helpful for their rehabilitation. However, the variation and complexity in speech emotion features, the extraction of which itself is a challenging task, will contribute to the difficulty to improve the recognition performance of the whole system. In view of this problem, this paper proposes a new method of speech emotion feature extraction with unsupervised auto-encoding network to learn emotional feature in speech signal automatically. By constructing a 3-layer auto-encoding network to extract the speech emotional feature, the high level feature is used as the input of extreme learning machine classifier to make final recognition. The speech emotion recognition rate of the system reaches 84.14%, which is higher than the traditional method based on human defined feature extraction.