A distributed framework for enterprise level speech recognition services

The paper presents methods for improving the efficiency of automatic speech recognition (ASR) decoders in multiuser applications. The methods involve allocating ASR resources to service human-machine dialogs in deployments that make use of many low-cost, commodity servers. It is shown that even very simple strategies for efficient allocation of ASR servers to incoming utterances has the potential to double the capacity of a multiuser deployment. This is important because, while there has been a great deal of work applied to increasing the efficiency of individual ASR engines, there has been little effort applied to increasing overall efficiency at peak loads in multiuser scenarios.