NDLI: Structured Output Layer neural network language model

Content Provider	IEEE Xplore Digital Library
Author	Hai-Son Le Oparin, I. Allauzen, A. Gauvain, J.-L. Yvon, F.
Copyright Year	2011
Description	Author affiliation: Univ. Paris-Sud, B.P. 133, 91403 Orsay, cedex, France (Hai-Son Le; Allauzen, A.; Yvon, F.) \|\| LIMSI CNRS, Spoken Language Processing Group, B.P. 133, 91403 Orsay, cedex, France (Oparin, I.; Gauvain, J.-L.)
Abstract	This paper introduces a new neural network language model (NNLM) based on word clustering to structure the output vocabulary: Structured Output Layer NNLM. This model is able to handle vocabularies of arbitrary size, hence dispensing with the design of short-lists that are commonly used in NNLMs. Several softmax layers replace the standard output layer in this model. The output structure depends on the word clustering which uses the continuous word representation induced by a NNLM. The GALE Mandarin data was used to carry out the speech-to-text experiments and evaluate the NNLMs. On this data the well tuned baseline system has a character error rate under 10%. Our model achieves consistent improvements over the combination of an n-gram model and classical short-list NNLMs both in terms of perplexity and recognition accuracy.
Starting Page	5524
Ending Page	5527
File Size	121884
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5947610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Vocabulary Training Context Computational modeling Speech recognition Adaptation models Speech-To-Text Neural Network Language Model Automatic Speech Recognition
Content Type	Text
Resource Type	Article

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