The　auto-regressive　(AR)　model　is　an　effective　method　to　describe　the　correlation　of　time　series.The　classic　AR　coefficient　estimation　method　utilizes　a　simple　assumption　about　residual　signal.It　is　a　challenge　to　accurately　estimate　the　auto-regressive　coefficients　in　a　complex　environment　such　as　noise　or　interference.Even　though　Deep　Neural　Networks　(DNN)based　AR　(DNN-AR)　coefficient　estimation　method　can　estimate　the　AR　coefficients　in　a　complex　environment,the　DNN-AR　method　is　easily　affected　by　the　numerical　stability　of　Levinson-Durbin　recursion　(LDR)　approach　during　the　training　stage.The　main　target　is　to　improve　the　stability　and　overall　performance　of　the　DNN-AR　based　method.In　this　paper,the　precision　transform　method　is　utilized　to　improve　computational　efficiency　while　keeping　system　stability,and　the　generalized　analysis-by-synthesis　combing　DNN　(GABS-DNN)　model　is　proposed　for　improving　the　accuracy　of　AR　coefficient　estimation　and　stability　of　the　DNN　training　in　the　noisy　environment.The　GABS-DNN　model　consists　of　three　main　parts:spectrum　enhancement　network　in　the　modifier,DNN　preprocessing　and　LDR　parameter　estimation　at　the　encoder,and　the　conversion　from　autoregressive　coefficient　to　power　spectrum　at　the　decoder.In　the　process　of　optimizing　the　objective　function,the　error　between　the　enhanced　spectrum　and　the　observed　spectrum　is　added　for　reducing　the　influence　of　the　gradient　of　the　LDR　on　the　enhanced　network　during　back-propagation,which　results　in　a　stable　estimation　of　the　AR　coefficients　of　noisy　speech.　©　2021,　Chinese　Institute　of　Electronics.　All　right　reserved.