Scaling Smoothed Language Models

In Continuous Speech Recognition (CSR) systems a Language Model (LM) is required to represent the syntactic constraints of the language. Then a smoothing technique needs to be applied to avoid null LM probabilities. Each smoothing technique leads to a different LM probability distribution. Test set perplexity is usually used to evaluate smoothing techniques but the relationship with acoustic models is not taken into account. In fact, it is well-known that to obtain optimum CSR performances a scaling exponential parameter must be applied over LMs in the Bayes’ rule. This scaling factor implies a new redistribution of smoothed LM probabilities. The shape of the final probability distribution is due to both the smoothing technique used when designing the language model and the scaling factor required to get the optimum system performance when integrating the LM into the CSR system. The main object of this work is to study the relationship between the two factors, which result in dependent effects. Experimental evaluation is carried out over two Spanish speech application tasks. Classical smoothing techniques representing very different degrees of smoothing are compared. A new proposal, Delimited discounting, is also considered. The results of the experiments showed a strong dependence between the amount of smoothing given by the smoothing technique and the way that the LM probabilities need to be scaled to get the best system performance, which is perplexity independent in many cases. This relationship is not independent of the task and available training data.