Skip to Main content Skip to Navigation
Conference papers

Statistical Language Model based on a Hierarchical Approach : MCnv

Imed Zitouni 1 Kamel Smaïli 1 Jean-Paul Haton 1
1 PAROLE - Analysis, perception and recognition of speech
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : 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.
Document type :
Conference papers
Complete list of metadata
Contributor : Publications Loria Connect in order to contact the contributor
Submitted on : Tuesday, September 26, 2006 - 2:49:02 PM
Last modification on : Wednesday, February 2, 2022 - 3:51:52 PM


  • HAL Id : inria-00100677, version 1



Imed Zitouni, Kamel Smaïli, Jean-Paul Haton. Statistical Language Model based on a Hierarchical Approach : MCnv. 7th european conference on speech communication and technology - EUROSPEECH 2001, 2001, Aalborg, Denmark, pp.29. ⟨inria-00100677⟩



Record views