Integrating High and Low Smoothed LMs in a CSR System

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.