Architectures of neural networks applied for LVCSR language modeling
The n-gram model and its derivatives are both widely applied solutions for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, Slavonic languages require a language model that considers word order less strictly than English, i.e. the language that is the subject of most linguistic research. Such a language model is a necessary module in LVCSR systems, because it increases the probability of finding the right word sequences. The aim of the presented work is to create a language module for the Polish language with the application of neural networks. Here, the capabilities of Kohonen's Self-Organized Maps will be explored to find the associations between words in spoken utterances. To fulfill such a task, the application of neural networks to evaluate sequences of words will be presented. Then, the next step of language model development, the network architectures, will be discussed. The network proposed for the construction of the considered model is inspired by the CockeYoungKasami parsing algorithm.
language modeling speech recognition self-organized maps neural network architectures