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Automated depression detection is vital for early diagnosis, but ethical and privacy concerns often limit the availability of sufficient training data, hindering research in depression screening. To address this, we introduce DepressGEN, a novel framework that generates synthetic interview dialogue texts and speech simulating depressed patients to improve training for detection models. By inputting linguistic features associated with depression into a large language model, we create dialogue texts and use a TTS system to generate corresponding speech. We also developed a depression modulation module to modify the synthesized speech, as well as a speech verification module to bridge the gap between synthetic and real data distributions. Our results demonstrate that a GRU/BiLSTM-based model trained with additional synthetic data improves F1 scores by 9.9% compared to the same model trained only on original data, outperforming existing methods on the EATD dataset.