In　this　article,　the　direction　of　arrival　(DOA)　estimation　of　multiple　speech　sources　in　reverberant　environments　is　investigated　based　on　the　recording　of　a　soundfield　microphone.　First,　the　recordings　are　analyzed　in　the　time-frequency　(T-F)　domain　to　detect　both　＂points＂　(single　T-F　points)　and　＂regions＂　(multiple,　adjacent　T-F　points)　corresponding　to　a　single　source　with　low　reverberation　(known　as　low-reverberant-single-source　(LRSS)　points).　Then,　a　LRSS　point　detection　algorithm　is　proposed　based　on　a　joint　dominance　measure　and　instantaneous　single-source　point　(SSP)　identification.　Following　this,　initial　DOA　estimates　obtained　for　the　detected　LRSS　points　are　analyzed　using　a　Gaussian　Mixture　Model　(GMM)　derived　by　the　Expectation-Maximization　(EM)　algorithm　to　cluster　components　into　sources　or　outliers　using　a　rule-based　method.　Finally,　the　DOA　of　each　actual　source　is　obtained　from　the　estimated　source　components.　Experiments　on　both　simulated　data　and　data　recorded　in　an　actual　acoustic　chamber　demonstrate　that　the　proposed　algorithm　exhibits　improved　performance　for　the　DOA　estimation　in　reverberant　environments　when　compared　to　several　existing　approaches.