In Continuous Speech Recognition (CSR) systems,
acoustic and Language Models (LM) must be integrated.  To get optimum
CSR performances, it is well-known that heuristic factors must be
optimised. Due to its great effect on final CSR
performances, the exponential scaling factor applied to LM
probabilities is the most important.  LM probabilities are
obtained after applying a smoothing technique. The use of the scaling
factor implies a redistribution of the smoothed LM probabilities,
i.e., a new smoothing is obtained.  In this work, the relationship
between the amount of smoothing of LMs and the new smoothing achieved
by the scaling factor is studied.  High and low smoothed LMs, using
well-known discounting techniques, were integrated into the CSR system.  The
experimental evaluation was carried out on two Spanish speech
application tasks with very different levels of difficulty.  The
strong relationship observed between the two redistributions of the LM
probabilities was independent of the task.  When the adequate value of
the scaling factor was applied, not very different optimum CSR
performances were obtained in spite of the great differences between
perplexity values.