Statistical methods for speech recognition
Statistical methods for speech recognition
Self-Organizing Maps
Pattern Recognition in Speech and Language Processing
Pattern Recognition in Speech and Language Processing
A Neural Syntactic Language Model
Machine Learning
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Springer Handbook of Speech Processing
Springer Handbook of Speech Processing
Neural network based language models for highly inflective languages
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Using semantic analysis to improve speech recognition performance
Computer Speech and Language
New directions in connectionist language modeling
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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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 Cocke-Young-Kasami parsing algorithm.