Acoustic characteristics are often changed over time as a result of various factors including changes of speakers, speaking styles, and noise sources. Incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models quickly and stably to such time-variant acoustic characteristics. Recently we proposed a novel incremental adaptation framework based on a macroscopic time evolution system, which models the time-variant characteristics by successively updating posterior distributions of acoustic model parameters. This paper proposes fast incremental adaptation based on a macroscopic time evolution system that realizes an utterance-by-utterance update by approximating the posterior distributions. This adaptation was used to perform on-line adaptation of Japanese broadcast news for very large vocabulary continuous speech recognition (700k vocabulary size) in real time. The word accuracy was improved from 73.9% to 85.1%. In addition, by incorporating a Bayesian model selection approach, we realized the simultaneous on-line adaptation and detection of environmental changes.