In　conventional　codebook-driven　speech　enhancement,　only　spectral　envelopes　of　speech　and　noise　are　considered,　and　at　the　same　time,　the　type　of　noise　is　the　priori　information　when　we　enhance　the　noisy　speech.　In　this　paper,　we　propose　a　novel　codebook-based　speech　enhancement　method　which　exploits　a　priori　information　about　binaural　cues,　including　clean　cue　and　pre-enhanced　cue,　stored　in　the　trained　codebook.　This　method　includes　two　main　parts:　offline　training　of　cues　and　online　enhancement　by　means　of　cues.　That　is,　we　use　the　trained　codebook　to　model　a　priori　information　of　speech　and　noise　offline　and　extract　the　pre-enhanced　cue　from　the　noisy　observation　online.　The　clean　cue　is　estimated　by　the　mapping　of　the　weighted　code　vectors　online,　and　the　enhanced　speech　is　produced　by　the　estimated　clean　cue.　The　experimental　results　show　that　the　proposed　approach　performs　better　than　the　reference　methods　in　both　stationary　and　non-stationary　noise　condition.　©　2016　IEEE.