In　this　paper,　a　hidden　Markov　model　(HMM)-based　cue　parameters　estimation　method　for　single-channel　speech　enhancement　is　proposed,　in　which　the　cue　parameters　of　binaural　cue　coding　(BCC)　are　applied　to　single-channel　speech　enhancement　system　successfully.　First,　the　clean　speech　and　noise　signals　are　considered　as　the　left　and　right　channels　of　stereo　signal,　respectively;　and　the　noisy　speech　is　treated　as　the　down-mixed　mono　signal　of　BCC　method.　According　to　the　clean　speech　and　noise　data　set　and　the　corresponding　noisy　speech　data　set,　the　clean　cue　parameters　and　pre-enhanced　cue　parameters　are　extracted,　respectively.　Then　the　cue　HMM　is　trained　offline,　which　exploits　the　a　priori　information　about　the　clean　cue　parameters　and　the　pre-enhanced　cue　parameters　for　speech　enhancement.　Next,　using　the　trained　cue　HMM,　the　clean　cue　parameters　are　estimated　from　noisy　speech　online.　Finally,　following　the　synthesis　principle　of　BCC　cue　parameters,　the　speech　estimator　is　constructed　for　enhancing　noisy　speech.　The　test　results　demonstrate　that,　for　the　segmental　signal-noise-ratio　(SNR),　the　log　spectral　distortion　and　PESQ　measures,　the　proposed　method　performs　better　than　the　reference　methods.　©　2016　IEEE.