To　identify　the　localization　of　indoor　sound　source,　especially　when　attempted　using　only　a　single　microphone,　it　is　a　challenging　problem　to　machine　learning.　To　address　these　issues,　this　paper　presents　a　distinct　novel　solution　based　on　fusing　visual　and　acoustic　models.　Therefore,　we　propose　two　novel　approaches.　First,　to　estimate　orientation　of　vocal　object　in　a　stable　manner,　we　employ　the　visual　approach　as　estimation　model,　where　we　develop　a　robust　image　feature　representation　method　that　adopts　Fourier　analysis　to　efficiently　extract　polar　descriptors.　Second　the　distance　information　is　estimated　by　calculating　the　signal　difference　between　transmit　receive　ends.　To　implement　these,　we　use　phoneme-level　hidden　Markov　models　(HMMs)　extracted　from　clean　speech　sound,　to　estimate　the　acoustic　transfer　function　(ATF),　which　can　capture　the　speech　signal　as　a　network　of　phoneme　HMMs.　And　using　the　separated　frame　sequences　of　the　ATF,　we　can　indicate　the　signal　difference　between　two　positions,　which　can　be　used　to　estimate　the　distance　of　sound　source.　Experimental　results　show　that　the　proposed　method　can　simultaneously　extract　the　sound　source　parameters　of　direction　and　distance,　and　thus　improves　the　verification　task　of　sound　source　localization.　©　2021　Elsevier　Ltd