Fertility channel model for post-correction of continuous speech recognition

We have implemented a post-processor called SPEECHPP to comet word-level errors committed by an arbitrary speech recognizer. Ap plying a noisy-channelmodel, SPEECHPP uses a Viterbi beam-search that employs language and channel models. Previous work demonstrated that a simple word-for-word channel model was sufficient to yield substantial increases in word accuracy. This paper demonstrates that some improvements in word accuracy result from augmenting the channel model with an account of word fertility in the channel. This work further demonstrates that a modem continuous speech recognizer can be used in ''black-box'' fashion for robustly recognizing speech for which the recognizer was not originally trained. This work also demonstrates that in the case where the recognizer can be tuned to the new task, environment or speaker, the post-processor can also contribute to performance improvements.