Tri-factorization learning of sub-word units with application to vocabulary acquisition

In prior work, we proposed a method for vocabulary acquisition based on a co-occurrence model and non-negative matrix factorization. The vocabulary is described in terms of co-occurrence statistics of frame-level acoustic descriptions and suffers from poor scalability to larger vocabularies. Much like whole-word HMM models, there is no reuse of a sub-word units such as phone models. In this paper, we apply the co-occurrence framework to learn a set of sub-word units unsupervisedly using a matrix tri-factorization and propose a method for computing their posteriorgram and finally show vocabulary acquisition from the posteriorgram. The method outperforms our prior work in that it can learn from a smaller set of labeled data and shows a better recognition accuracy.