Automatic Speech Recognition and Speech Activity Detection in the CHIL Smart Room

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.