Booosting word error rates
Christos Dimitrakakis and Samy Bengio
In: ICASSP 2005, March 2004, Philadelphia, United States.
We apply boosting techniques to the problem of word error
rate minimisation in speech recognition. This is achieved through a
new definition of sample error for boosting and a training procedure
for hidden Markov models. For this purpose we define a sample error
for sentence examples related to the word error rate. Furthermore,
for each sentence example we define a probability distribution in
time that represents our belief that an error has been made at that
particular frame. This is used to weigh the frames of each sentence
in the boosting framework. We present preliminary results on the
well-known Numbers 95 database that indicate the importance of this
temporal probability distribution.