Early　intervention　for　depression　is　very　important　to　ease　the　disease　burden,　but　current　diagnostic　methods　are　still　limited.　This　study　investigated　automatic　depressed　speech　classification　in　a　sample　of　170　native　Chinese　subjects　(85　healthy　controls　and　85　depressed　patients).　The　classification　performances　of　prosodic,　spectral,　and　glottal　speech　features　were　analyzed　in　recognition　of　depression.　We　proposed　an　ensemble　logistic　regression　model　for　detecting　depression　(ELRDD)　in　speech.　The　logistic　regression,　which　was　superior　in　recognition　of　depression,　was　selected　as　the　base　classifier.　This　ensemble　model　extracted　many　speech　features　from　different　aspects　and　ensured　diversity　of　the　base　classifier.　ELRDD　provided　better　classification　results　than　the　other　compared　classifiers.　A　technique　for　identifying　depression　based　on　ELRDD,　ELRDD-E,　was　here　suggested　and　tested.　It　offered　encouraging　outcomes,　revealing　a　high　accuracy　level　of　75.00%　for　females　and　81.82%　for　males,　as　well　as　an　advantageous　sensitivity/specificity　ratio　of　79.25%/70.59%　for　females　and　78.13%/85.29%　for　males.