"Clear" (CLR) speech is a speaking style that speakers adopt to be understood correctly in a difficult communication environment. Studies have shown that CLR speech, as opposed to "conversational" (cnv) speech, has significantly higher intelligibility in various conditions. While many differences in acoustic features have been identified, it is not known which individual feature or combinations of features cause the higher intelligibility of CLR speech. The objectives of the current study are to examine whether it is possible to improve speech intelligibility by approximating CLR speech features and to determine which acoustic features contribute to intelligibility. Our approach creates speech samples that combine acoustic features of CNV and CLR speech, using a hybridization algorithm. Results with normalhearing listeners showed significant sentence-level intelligibility improvements of 11-23% over CNV speech when replacing certain acoustic features with those from CLR speech.