Random Clusterings for Language Modeling
Ahmad Emami, F. Jelinek · 2006
We present an application of randomization techniques to class-based n-gram language models used in speech recognizers. The idea is to derive a language model from the combination of a set of random class-based models. Each of the constituent random class-based models is built using a separate clustering obtained via a different run of a randomized clustering algorithm. The random class-based model can compensate for some of the shortcomings of conventional class-based models by combining the different solutions obtained through random clusterings. Experimental results show that the combined random class-based model improves considerably in perplexity (PPL) and word error rate (WER) over both the n-gram and baseline class-based models.