Low resource speech recognition is one of currently researching hotspots in speech recognition community, and is also one of the important challenges for the application of multilingual and minority language speech recognition technologies. This paper summarizes and reviews the current states and history of low resource speech recognition, and introduces several key technologies, including articulatory feature, multilingual bottleneck feature, subspace Gaussian mixture model, convolutional neural network based acoustic model and recurrent neural network based language model. After that the open keyword search (OpenKWS) evaluation is introduced. Finally, the prospective of low resource speech recognition is presented.