VARIABLE-LENGTH SEQUENCE MODELING - MULTIGRAMS

The conventional n-gram language model exploits dependencies between words and their fixed-length past, This letter presents a model that represents sentences as a concatenation of variable-length sequences of units and describes an algorithm for unsupervised estimation of the model parameters. The approach is illustrated for the segmentation of sequences of letters into subword-like units, It is evaluated as a language model on a corpus of transcribed spoken sentences. Multigrams can provide a significantly lower test set perplexity than n-gram models.