This study investigates the signal processing required in order to allow for the evaluation of hearing perception prediction models at low signal-to-noise Ratios (SNR). It focusses on speech enhancement and the estimation of the cues from which speech may be recognized, specifically where these cues are estimated from severely degraded speech (SNR ranging from -10 dB to -3 dB). This research has application in the field of cochlear implants (CI), where a listener would hear degraded speech due to several distortions introduced by the biophysical interface (e.g. frequency and amplitude discretization). These difficulties can also be interpreted as a loss in signal quality due to a specific type of noise. The ability to investigate perception in low SNR conditions may have application in the development of CI signal processing algorithms to counter the effects of noise. In the military domain a speech signal may be degraded intentionally by enemy forces or unintentionally owing to engine noise, for example. The ability to analyse and predict perception can be used for algorithm development to counter the unintentional or intentional interference or to predict perception degradation if low SNR conditions cannot be avoided. A previously documented perception model (Svirsky, 2000) is used to illustrate that the proposed signal processing steps can indeed be used to estimate the various cues used by the perception model at SNRs successfully as low as -10 dB. AFRIKAANS : Hierdie studie ondersoek die seinprosessering wat nodig is om ’n gehoorpersepsievoorspellingmodel te evalueer by lae sein-tot-ruis-verhoudings. Hierdie studie fokus op spraakverbetering en die estimasie van spraakeienskappe wat gebruik kan word tydens spraakherkenning, spesifiek waar hierdie eienskappe beraam word vir ernstig gedegradeerde spraak (sein-tot-ruisverhoudings van -10 dB tot -3 dB). Hierdie navorsing is van toepassing in die veld van kogleêre inplantings, waar die luisteraar degradering van spraak ervaar weens die bio-fisiese koppelvlak (bv. diskrete frekwensie en amplitude). Hierdie degradering kan gesien word as ’n verlies aan seinkwaliteit weens ’n spesifieke tipe ruis. Die vermoë om persepsie te ondersoek by lae sein-tot-ruis kan toegepas word tydens die ontwikkeling van kogleêre inplantingseinprosesseringalgoritmes om die effekte van ruis teen te werk. In die militêre omgewing kan spraak deur vyandige magte gedegradeer word, of degradering van spraak kan plaasvind as gevolg van bv. enjingeraas. Die vermoë om persepsie te ondersoek en te voorspel in die teenwoordigheid van ruis kan gebruik word vir algoritme-ontwikkeling om die ruis teen te werk of om die verlies aan persepsie te voorspel waar lae sein-tot-ruis verhoudings nie vermy kan word nie. ’n Voorheen gedokumenteerde persepsiemodel (Svirsky, 2000) word gebruik om te demonstreer dat die voorgestelde seinprosesseringstappe wel suksesvol gebruik kan word om die spraakeienskappe te beraam wat deur die persepsiemodel benodig word by sein-tot-ruis verhouding so laag as -10 dB. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted