The purpose of this study is to create information and signal processing algorithms to enhance the interpretation of speech in noise. An algorithm based on time-frequency analysis was employed to extract quasi-steady-state (QSS) energy from the speech signal, leaving a residual signal of predominantly transient components. The transient component was selectively amplified and recombined with the original speech to generate enhanced speech. The energy of the enhanced speech was adjusted to be equal to that of the original speech, and the intelligibility of the enhanced speech was compared to that of the original speech in background noise. Psychometric functions showed the enhanced speech had higher recognition scores at most noise levels, with greater improvement at lower SNRs. These results suggest that subjects can identify enhanced speech better than the original speech at severe noise conditions