Abstract: To achieve good reconstruction speech quality in very low bit rate speech codecs, an efficient dimension reduction quantization scheme for linear spectrum pair (LSP) parameters was proposed based on compressed sensing. In the encoder, the LSP parameters extracted from consecutive speech frames are shaped into a high dimensional vector, and then the dimension of the vector is reduced by CS to produce a measurement vector, the measurements are quantized using the split vector quantizer. In the decoder, according to the quantized measurements, the original LSP vector is reconstructed by the orthogonal matching pursuit method. Experimental results show that the scheme is more efficient than that of conventional matrix quantization scheme and DCT based dimension reduction quantization scheme, the average spectral distortion reduction of up to 0.23dB and 0.13dB is achieved respectively. Informal subjective listening test shows that the reconstructed speech has moderate intelligibility and naturalness, it is observed that the degradation in speech quality is tolerable and with low codebook storage requirements.