CUED Publications database

CUED-RNNLM - An open-source toolkit for efficient training and evaluation of recurrent neural network language models

Chen, X and Liu, X and Qian, Y and Gales, MJF and Woodland, PC (2016) CUED-RNNLM - An open-source toolkit for efficient training and evaluation of recurrent neural network language models. In: UNSPECIFIED pp. 6000-6004..

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Abstract

© 2016 IEEE. In recent years, recurrent neural network language models (RNNLMs) have become increasingly popular for a range of applications including speech recognition. However, the training of RNNLMs is computationally expensive, which limits the quantity of data, and size of network, that can be used. In order to fully exploit the power of RNNLMs, efficient training implementations are required. This paper introduces an open-source toolkit, the CUED-RNNLM toolkit, which supports efficient GPU-based training of RNNLMs. RNNLM training with a large number of word level output targets is supported, in contrast to existing tools which used class-based output-targets. Support fotN-best and lattice-based rescoring of both HTK and Kaldi format lattices is included. An example of building and evaluating RNNLMs with this toolkit is presented for a Kaldi based speech recognition system using the AMI corpus. All necessary resources including the source code, documentation and recipe are available online1.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:00
Last Modified: 26 Sep 2017 01:41
DOI: