When people communicate in noisy environments with the phone, it is difficult for listeners to obtain information even if the device outputs clear speech. Previous studies have focused on speech intelligibility enhancement (IENH) via normal speech and different levels of Lombard speech conversion. However, these methods often lead to speech distortion and impair the overall speech quality. We propose an IENH framework based on an improved Star Generative Adversarial Network (StarGAN) named D2StarGAN. It has two main advantages: 1) Inspired by the dual-discriminator idea, we add a speech metric discriminator based on StarGAN to optimize multiple intelligibility-related metrics simultaneously; 2) The framework can adapt to different far-and-near-end noise levels and different noise types. Experimental results using both objective measurements and subjective listening tests indicate that the proposed method outperforms the baseline method. It adaptively converts all the mobile communication scenarios with far-and-near-end noise, thus making IENH more widely used in practice.