Publication detail

Recurrent Neural Network based Language Modeling in Meeting Recognition

KOMBRINK, S. MIKOLOV, T. KARAFIÁT, M. BURGET, L.

Original Title

Recurrent Neural Network based Language Modeling in Meeting Recognition

English Title

Recurrent Neural Network based Language Modeling in Meeting Recognition

Type

conference paper

Language

en

Original Abstract

In this paper we recommend the use of RNN language models as easy mean to improve an existing LVCSR system, either by improving ngram models using data sampled from an RNN or by performing the proposed rescoring and adaptation postprocessing steps.

English abstract

In this paper we recommend the use of RNN language models as easy mean to improve an existing LVCSR system, either by improving ngram models using data sampled from an RNN or by performing the proposed rescoring and adaptation postprocessing steps.

Keywords

automatic speech recognition, language modeling, recurrent neural networks, rescoring, adaptation

RIV year

2011

Released

27.08.2011

Publisher

International Speech Communication Association

Location

Florence

ISBN

978-1-61839-270-1

Book

Proceedings of Interspeech 2011

Edition

NEUVEDEN

Edition number

NEUVEDEN

Pages from

2877

Pages to

2880

Pages count

4

URL

Documents

BibTex


@inproceedings{BUT76441,
  author="Stefan {Kombrink} and Tomáš {Mikolov} and Martin {Karafiát} and Lukáš {Burget}",
  title="Recurrent Neural Network based Language Modeling in Meeting Recognition",
  annote="In this paper we recommend the use of RNN language models as easy mean to improve
an existing LVCSR system, either by improving ngram models using data sampled
from an RNN or by performing the proposed rescoring and adaptation postprocessing
steps.",
  address="International Speech Communication Association",
  booktitle="Proceedings of Interspeech 2011",
  chapter="76441",
  edition="NEUVEDEN",
  howpublished="print",
  institution="International Speech Communication Association",
  number="8",
  year="2011",
  month="august",
  pages="2877--2880",
  publisher="International Speech Communication Association",
  type="conference paper"
}