Abstract
N-grams have been extensively and successfully used for language modelling in continuous speech recognition tasks. On the other hand, it has been shown that k-testable stochastic languages (k-TS) are strictly equivalent to N-grams. A major problem to be solved when using a language model is the estimation of the probabilities of events not represented in the training corpus, i.e. unseen events. The aim of this work is to improve other well established smoothing procedures by interpolating models with different levels of complexity (quality weighted interpolation-QWI). The effect of QWI was experimentally evaluated over a set of back-off smoothed k-TS language models. These experiments were carried out over several corpora using the test-set perplexity as an evaluation criterion. In all the cases the introduction of QWI resulted in a reduction of the test-set perplexity.
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