An important step to bring speech technologies into wide deployment as a functional component in man-machine interfaces is to free the users from close-talk or desktop microphones, and enable far-field operation in various natural communication environments. In this work, we consider far-field automatic speech recognition and speech activity detection in conference rooms. The experiments are conducted on the smart room platform provided by the CHIL project.  The first half of the paper addresses the development of speech recognition systems for the seminar transcription task. In particular, we look into the effect of combining parallel recognizers in both single-channel and multi-channel settings.  In the second half of the paper, we describe a novel algorithm for speech activity detection based on fusing phonetic likelihood scores and energy features. It is shown that the proposed technique is able to handle non-stationary noise events and achieves good performance on the CHIL seminar corpus.