In　this　paper,　a　method　for　estimating　the　autoregressive　parameters　from　a　signal　segment　is　proposed.　The　method　is　based　on　a　deep　neural　network　(DNN)　in　combination　with　the　classical　Levinson-Durbin　recursion　(LDR).　The　DNN　acts　as　a　pre-processor　for　the　LDR　and　can　be　trained　on　different　metrics　commonly　encountered　in　speech　processing　using　a　generalized　analysis-by-synthesis　(GABS)　structure　where　the　LDR　acts　as　the　encoder.　Unlike　end-to-end　data-driven　approaches,　this　structure　ensures　that　the　DNN　is　easy　to　train　and　initialize　since　the　DNN　only　has　to　learn　a　simple　mapping.　The　results　confirm　this　and　show　that　the　proposed　method　produces　an　AR-spectrum　that　efficiently　represents　the　speech　spectrum　in　terms　of　the　Itakura-Saito　divergence,　Kullback-Leibler　divergence,　log-spectral　distortion,　and　speech　distortion.　©　2020　IEEE.