Speech intelligibility enhancement is a perceptual enhancement technique for clean speech reproduced in noisy environments. Speaking style conversion (SSC) is used in many studies to achieve speech intelligibility, which relies solely on the Lombard effect and thus demonstrates poor performance with strong noise interference. In addition, the SSC method models the conversion of fundamental frequency (F0) with a straight forward linear transform and only maps Mel-frequency cepstral coefficients (MFCCs) with few dimensions. As F0 and MFCCs are critical aspects of hierarchical intonation, adequate modeling of these features is essential. Therefore, we use the continuous wavelet transform (CWT) to decompose F0 into ten dimensions to describe speech at different time scales for effective F0 conversion and represent MFCCs with 20 dimensions for MFCC conversion. Furthermore, we utilize an iMetricGAN to optimize speech intelligibility metrics in strong noise. The experimental results show that in objective and subjective evaluations, the proposed non-parallel speech style conversion method using CWT and iMetricGAN based on CycleGAN (NS-CiC) significantly increases speech intelligibility in robust noise environments.