In this paper, single channel speech separation (SCSS) techniques based on hidden Markov models (HMM) and vector quantization (VQ) are described and compared in terms of (a) signal-to-noise ratio (SNR) between separated and original speech signals, (b) preference of listeners, and (c) computational complexity. The SNR results show that the HMM-based technique marginally outperforms the VQ-based technique by 0.85 dB in experiments conducted on mixtures of female-female, male-male, and male-female speakers. Subjective tests show that listeners prefer HMM over VQ for 86.70% of test speech files. This improvement, however, is at the expense of a drastic increase in computational complexity when compared with the VQ-based technique.