ACL RD-TEC 1.0 Summarization of H05-1026

Paper Title:
TRAINING NEURAL NETWORK LANGUAGE MODELS ON VERY LARGE CORPORA

Authors: Holger Schwenk and Jean-Luc Gauvain

Other assigned terms:

  • 4-gram back-off lm
  • acoustic model
  • acoustic models
  • acoustic signal
  • approach
  • association for computational linguistics
  • broadcast news
  • broadcast news data
  • brown corpus
  • cluster
  • coefficient
  • continuous speech
  • convergence
  • corpora
  • data sparseness
  • data sparseness problem
  • data structures
  • development set
  • entropy
  • error rate
  • estimation
  • experimental results
  • fact
  • feature
  • feature vectors
  • french
  • french broadcast news
  • grammar
  • hypothesis
  • interpolation
  • interpolation coefficients
  • knowledge
  • language model
  • language models
  • large corpora
  • large corpus
  • large text corpora
  • large training
  • large training corpora
  • lattice
  • lattices
  • learning rate
  • linguistics
  • n-gram
  • n-gram model
  • n-grams
  • natural language
  • network architecture
  • neural network architecture
  • noise
  • perplexity
  • posterior
  • probabilities
  • probability
  • probability distributions
  • procedure
  • processing time
  • projection
  • pronunciation
  • sentence
  • signal
  • sparseness problem
  • speaking style
  • speech signal
  • statistics
  • style
  • support vector
  • technique
  • technology
  • term
  • text
  • text corpora
  • theory
  • toolkit
  • training
  • training corpora
  • training data
  • training example
  • training examples
  • training set
  • training time
  • transcriptions
  • transcripts
  • tree
  • vocabulary
  • word
  • word error rate
  • word error rates
  • word lattice
  • word sequence
  • words

Extracted Section Types:


This page last edited on 10 May 2017.

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