In this paper, we present corpora and recognition experiments of the speech recorded in everyday life for the real world speech recognition. A speech corpus of (8),600 sentences from 53 speakers recorded through lavalier microphones in four different environments is built. The data was collected in an office space, a sound-proof room, in cars of different sizes, and on the street. Another corpus consisting of whispered and normal speech of more than (6),000 sentences from 100 speakers recorded through a close-talking microphone is devised. Continuous speech recognition experiments using acoustic models trained by the speech corpus in each environment, attain a recognition accuracy of above 80%. For the whispered speech corpus, the recognition accuracy obtained was 74% using the whispered speech model.