This paper presents a study on the use of wide-coverage semantic knowledge for large vocabulary (theoretically unrestricted) domain-independent speech recognition. A machine readable dictionary was used to provide the semantic information about the words and a semantic model was developed based on the conceptual association between words as computed directly from the textual representations of their meanings. The findings of our research suggest that the model is capable of capturing phenomena of semantic associativity or connectivity between words in texts and considerably reducing the semantic ambiguity in natural language. The model can cover both short and long-distance semantic relationships between words and has shown signs of robustness across various text genres. Experiments with simulated speech recognition hypotheses indicate that the model can efficiently be used to reduce the word error rates when applied to word lattices or N-best sentence hypotheses.