Functional decline is one of the serious syndromes experienced among older adults. Its early assessment is critical to preventing its symptoms. Some Comprehensive Geriatric Assessment CGA questionnaires, chosen amongst others, can be performed as in-home self-assessments by older adults using QuestIO, a device based on automatic speech recognition ASR. This paper investigates the performance of the ASR on English Isolated words while using different features; Mel Frequency Cepstral Coefficient (MFCC), Relative spectra-perceptual linear prediction (RASTA-PLP), Perceptual linear prediction (PLP), Linear Prediction Cepstral Coefficients (LPCCs) or a combination of these, and the random forest classifier, to select the features that give the best performance. The performance was obtained based on the word recognition rate WRR and the real-time factor RTF. As a result, we selected the MFCC and RASTA-PLP cepstral coefficients. The WRR reached for these features is 96.57% with an RTF of 11×10-4.