A quality estimation (QE) approach informed with machine translation (MT) and speech recognition (ASR) features has recently shown to improve the performance of a spoken language translation (SLT) system in an in-domain scenario. When domain mismatch is progressively introduced in the MT and ASR systems, the SLT system's performance naturally degrades. The use of QE to improve SLT performance has not been studied in this context. In this paper we investigate the effectiveness of QE under this setting. Our experiments showed that across moderate levels of domain mismatches, QE led to consistent translation improvements of around 0.4 in BLEU score. The QE system relies on 116 features derived from the ASR and MT system input and output. Feature analysis was conducted to understand the information sources contributing the most to performance improvements. LDA dimension reduction was used to summarise effective features into sets as small as 3 without affecting the SLT performance. By inspecting the principal components, eight features including the acoustic model scores and count-based word statistics on the bilingual text were found to be critically important, leading to a further boost of around 0.1 BLEU score over the full set of features. These findings provide interesting possibilities for further work by incorporating the effective QE features in SLT system training or decoding.