Abstract: Compressed Sensing (CS) is a recently proposed theory that enables the exact reconstruction of signal sampled via subNyquist sampling rate. The theory has been applying for simplifying the traditional sampling hardware，reducing sampling time consumption and decreasing storage space of data. Benefiting from the superiority of CS technique for speech signal transmission, we propose an adaptive inter-frame speech compressed sensing method. Under the assumption that speech signal is sparse in Discrete Cosine Transform (DCT) domain and according to the statistical behavior of speech signal frames, our proposed method takes into account both intra-frame energy and inter-frame consecution of location in our adaptive compressed sensing algorithm. Experimental results show that, the method of intra-frame energy adaption can promote the speech recovery quality apparently and the method of location adaption can reduce the speech recovery time obviously. Namely, the proposed adaptive compressed sensing algorithm in this letter can achieve higher speech reconstruction performance with less time consuming.