This paper sets out to study several important aspects pertaining to speech recognition errors, especially the out-of-vocabulary (OOV) word problem that is caused by using generic speech recognition systems for a specific application domain. To this end, a two-stage processing method, involving error detection and error correction, is proposed. For error detection, we explore and compare disparate sequence labeling methods to detect possible errors of different types. Further, in the error correction stage, an effective phone-level matching mechanism along with a domain-specific keyword list is exploited to correct errors of different types detected by the previous stage. Extensive experiments conducted on four application domains, including educational issues, industrial technology-related interviews and speech memos and meeting recordings, show that our proposed methods can boot the performance of a given general speech recognition system on the aforementioned application domains to some extent.