Lattice rescoring strategies for long short term memory language models in speech recognition

Shankar Kumar, M. Nirschl, D. Holtmann-Rice, H. Liao, A. Suresh, Felix X. Yu
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引用次数: 36

Abstract

Recurrent neural network (RNN) language models (LMs) and Long Short Term Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform traditional N-gram LMs on speech recognition tasks. However, these models are computationally more expensive than N-gram LMs for decoding, and thus, challenging to integrate into speech recognizers. Recent research has proposed the use of lattice-rescoring algorithms using RNNLMs and LSTMLMs as an efficient strategy to integrate these models into a speech recognition system. In this paper, we evaluate existing lattice rescoring algorithms along with new variants on a YouTube speech recognition task. Lattice rescoring using LSTMLMs reduces the word error rate (WER) for this task by 8% relative to the WER obtained using an N-gram LM.
语音识别中长短时记忆语言模型的点阵评分策略
递归神经网络(RNN)语言模型(LMs)和长短期记忆(LSTM) LMs (RNN LMs的一种变体)在语音识别任务上的表现优于传统的N-gram LMs。然而,这些模型在解码方面的计算成本比N-gram lm要高,因此很难集成到语音识别器中。最近的研究已经提出使用使用rnnlm和lstmlm的格点评分算法作为将这些模型集成到语音识别系统中的有效策略。在本文中,我们评估了现有的点阵评分算法以及YouTube语音识别任务的新变体。与使用N-gram LM获得的错误率相比,使用lstmlm的点阵重新评分将该任务的单词错误率(WER)降低了8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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