3 research outputs found
Analyzing and Improving Statistical Language Models for Speech Recognition
In many current speech recognizers, a statistical language model is used to
indicate how likely it is that a certain word will be spoken next, given the
words recognized so far. How can statistical language models be improved so
that more complex speech recognition tasks can be tackled? Since the knowledge
of the weaknesses of any theory often makes improving the theory easier, the
central idea of this thesis is to analyze the weaknesses of existing
statistical language models in order to subsequently improve them. To that end,
we formally define a weakness of a statistical language model in terms of the
logarithm of the total probability, LTP, a term closely related to the standard
perplexity measure used to evaluate statistical language models. We apply our
definition of a weakness to a frequently used statistical language model,
called a bi-pos model. This results, for example, in a new modeling of unknown
words which improves the performance of the model by 14% to 21%. Moreover, one
of the identified weaknesses has prompted the development of our generalized
N-pos language model, which is also outlined in this thesis. It can incorporate
linguistic knowledge even if it extends over many words and this is not
feasible in a traditional N-pos model. This leads to a discussion of
whatknowledge should be added to statistical language models in general and we
give criteria for selecting potentially useful knowledge. These results show
the usefulness of both our definition of a weakness and of performing an
analysis of weaknesses of statistical language models in general.Comment: 140 pages, postscript, approx 500KB, if problems with delivery, mail
to [email protected]
Character Recognition
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field