The application discloses a multi-type acoustic feature integration method and system based on deep neural networks. The method and system include using labeled speech data set to train and build a multi-type acoustic feature integration model based on deep neural networks, to determine or update the network parameters of the multi-type acoustic feature integration model; the method and system includes inputting the multiple types of acoustic features extracted from the testing speech into the trained multi-type acoustic feature integration model, and extracting the deep integrated feature vectors in frame level or segment level. The solution supports the integrated feature extraction for multiple types of acoustic features in different kinds of speech tasks, such as speech recognition, speech wake-up, spoken language recognition, speaker recognition, and anti-spoofing etc. It encourages the deep neural networks to explore internal correlation between multiple types of acoustic features according to practical speech tasks, to improve the recognition accuracy and stability of speech applications.