In this paper, we propose a new language model based on dependent word sequences organized in a multi-level hierarchy. We call this model MC$_{n}^{\nu}$, where $n$ is the maximum number of words in a sequence and $\nu$ is the maximum number of levels. The originality of this model is its capacity to take into account dependent variable-length sequences for very large vocabularies. In order to discover the variable-length sequences and to build the hierarchy, we use a set of $233$ syntactic classes extracted from the $8$ French elementary grammatical classes. The MC$_{n}^{\nu}$ model learns hierarchical word patterns and uses them to reevaluate and filter the n-best utterance hypotheses outputted by our speech recognizer MAUD. The model has been trained on a corpus of $43$ million words extracted from a French newspaper and uses a vocabulary of $20000$ words. Tests have been conducted on $300$ sentences. Results achieved $17\%$ decrease in perplexity compared to an interpolated class trigram model. Rescoring the original n-best hypotheses resulted in an improvement of $5\%$ in accuracy.