A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is
The most widely-used evaluation metric for language models for speech recognition is the perplexity of test data. While perplexities can be calculated efficiently and without access to a speech recognizer, they often do not correlate well with speech recognition word-error rates. In this research, we attempt to find a measure that like perplexity is easily calculated but which better predicts speech recognition performance. We investigate two approaches; first, we attempt to extend perplexitydoi:10.1184/r1/6605324.v1 fatcat:o5ncq2mceffqtmejouj4ldmtuu