Depression　is　one　of　the　most　common　mental　disorders.　Early　intervention　is　very　important　for　reducing　the　burden　of　the　disease,　but　current　methods　of　diagnosis　remain　limited.　Previously,　acoustic　features　of　speech　have　been　identified　as　possible　cues　for　depression,　but　there　has　been　little　research　to　link　depression　with　speech　types　and　emotions.　This　study　investigated　acoustic　correlates　of　depression　in　a　sample　of　170　subjects　(85　depressed　patients　and　85　healthy　controls).　We　examined　the　discriminative　power　of　three　different　types　of　speech　(interview,　picture　description,　and　reading)　and　three　speech　emotions　(positive,　neutral,　and　negative)　using　different　classifiers,　with　male　and　female　subjects　modeled　separately.　We　observed　that　picture　description　speech　rendered　significantly　better　(p　＜　0.05)　classification　results　than　other　speech　types　for　males,　and　interview　speech　performed　significantly　better　(p　＜　0.05)　than　other　speech　types　for　females.　Based　on　speech　types　and　emotions,　a　new　computational　methodology　for　detecting　depression　(STEDD)　was　developed　and　tested.　This　new　approach　showed　a　high　accuracy　level　of　80.30%　for　males　and　75.96%　for　females,　with　a　desirable　sensitivity/specificity　ratio　of　75.00%/85.29%　for　males　and　77.36%/74.51%　for　females.　These　results　are　encouraging　for　detecting　depression,　and　provide　guidance　for　future　research.　(C)　2017　Elsevier　B.V.　All　rights　reserved.