1. A computer-implemented method comprising: providing particular audio data to each automated speech recognizer of a set of automated speech recognizers; before all of the automated speech recognizers have output a respective hypothesis for the particular audio data, determining that a particular automated speech recognizer of the set of automated speech recognizers has output a hypothesis for the particular audio data, and that a confidence value associated with the hypothesis that is output by the particular automated speech recognizer satisfies a particular confidence value threshold; and while at least one of the automated speech recognizers that has been provided the particular audio data is indicated as not yet finished generating a respective hypothesis for the particular audio data, and in response to determining that the particular automated speech recognizer of the set of automated speech recognizers has output the hypothesis for the particular audio data, and that the confidence value associated with the hypothesis that is output by the particular automated speech recognizer satisfies the particular confidence value threshold: providing the hypothesis that is output by the particular automated speech recognizer, of the set of automated speech recognizers, as a top speech recognition hypothesis; and transmitting a command to stop the at least one of the automated speech recognizers of the set of automated speech recognizers that has been provided the particular audio data and that is indicated as not yet finished generating the respective hypothesis for the particular audio data from finishing generating the respective hypothesis for the particular audio data.
2. The method of claim 1 , wherein each automated speech recognizer of the set of automated speech recognizers uses a different one of a plurality of language models.