Improvement of acoustic models in automatic speech recognition systems

This thesis explores the use of efficient acoustic modeling techniques to improve the performance of automatic speech recognition (ASR) systems. The principal idea behind this study is that the pronunciation of a word is not only affected by the variability of the speaker and the environment, but largely by the words that precede and follow it. Hence, to accurately represent a language, one should model the sounds in context with other sounds. Furthermore, due to the large amount of models produced when every sound is represented in every context it can appear in, one needs to use a clustering technique by which sounds of similar properties are grouped together so as to limit the number of models needed. The aim of this research is two fold: the first is to explore the effects of using context dependent models on the performance of an ASR system, the second is to combine the context dependent models pertaining to a specific sound in a complex structure to produce a context independent model containing contextual information. Two such complex structures are designed and their performance is tested