This　study　proposes　multiple-speech-source　direction　-of-arrival　(DOA)　estimation　based　on　the　distribution　characteristic　of　the　time-frequency　(TF)　point　dominated　by　a　single-source　component　(i.e.,　single-source　point,　SSP).　By　exploring　the　TF　distribution　characteristics　of　SSPs,　we　found　that　most　are　distributed　in　clusters　in　the　TF　domain.　Hence,　the　concept　of　a　single-source　cluster　(SSC)　is　given,　each　composed　of　adjacent　TF　points　from　one　dominant　sound　source.　Considering　that　SSCs　have　different　shapes　and　sizes,　an　SSC　detection　method　is　designed　based　on　point-to-cluster　expansion,　which　is　the　research　focus　of　this　article.　A　two-dimensional　Gaussian　function　is　introduced　to　model　the　theoretical　distribution　of　the　DOAs　of　SSPs,　and　a　cluster　expansion　rule　is　proposed　based　on　hypothesis　testing　of　the　DOA　of　a　source.　Two-dimensional　kernel　density　estimation　and　peak　search　are　adopted　to　estimate　the　DOAs　and　the　number　of　sources　using　the　detected　SSCs.　Experimental　results　in　both　simulated　and　real　environments　show　that　the　proposed　method　can　achieve　better　DOA　estimation　performance　than　some　current　techniques.