  A previous signal processing algorithm that aimed to enhance spectral changes
(SCE) over time showed benefit for hearing-impaired (HI) listeners to recognize
speech in background noise. In this work, the previous SCE was manipulated to
perform on target-dominant segments, rather than treating all frames equally.
Instantaneous signal-to-noise ratios (SNRs) were calculated to determine
whether the segments should be processed. Initially, the ideal SNR calculated
by the knowledge of premixed signals was introduced to the previous SCE
algorithm (SCE-iSNR). Speech intelligibility (SI) and clarity preference were
measured for 12 HI listeners in steady speech-spectrum noise (SSN) and six-talk
speech (STS) maskers, respectively. The results showed the SCE-iSNR algorithm
improved SI significantly for both maskers at high signal-to-masker ratios
(SMRs) and for STS masker at low SMRs, while processing effect on speech
quality was small. Secondly, the estimated SNR obtained from real mixtures was
used, resulting in another SCE-eSNR. SI and subjective rating on naturalness
and speech quality were tested for 7 HI subjects. The SCE-eSNR algorithm showed
improved SI for SSN masker at high SMRs and for STS masker at low SMRs, as well
as better naturalness and speech quality for STS masker. The limitations of
applying the algorithms are discussed.
