Dynamic search-space pruning for time-constrained speech recognition

摘要

In automatic speech recognition complex state spaces are searched during the recognition process. By limiting these search spaces the computation time can be reduced, but unfortunately the recognition rate mostly decreases, too. However, especially for time-critical recognition tasks a search-space pruning is necessary. Therefore, we developed a dynamic mechanism to optimize the pruning parameters for time-constrained recognition tasks, e.g. speech recognition for robotic systems, in respect to word accuracy and computation time. With this mechanism an automatic speech recognition system can process speech signals with an approximately constant processing rate. Compared to a system without such a dynamic mechanism and the same time available for computation, the variance of the processing rate is decreased greatly without a significant loss of word accuracy. Furthermore, the extended system can be sped up to real-time processing, if desired or necessary.