A post-processing system to yield reduced word error rates: Recognizer Output Voting Error Reduction (ROVER)

J. Fiscus
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引用次数: 1221

Abstract

Describes a system developed at NIST to produce a composite automatic speech recognition (ASR) system output when the outputs of multiple ASR systems are available, and for which, in many cases, the composite ASR output has a lower error rate than any of the individual systems. The system implements a "voting" or rescoring process to reconcile differences in ASR system outputs. We refer to this system as the NIST Recognizer Output Voting Error Reduction (ROVER) system. As additional knowledge sources are added to an ASR system (e.g. acoustic and language models), error rates are typically decreased. This paper describes a post-recognition process which models the output generated by multiple ASR systems as independent knowledge sources that can be combined and used to generate an output with reduced error rate. To accomplish this, the outputs of multiple of ASR systems are combined into a single, minimal-cost word transition network (WTN) via iterative applications of dynamic programming (DP) alignments. The resulting network is searched by an automatic rescoring or "voting" process that selects the output sequence with the lowest score.
降低单词错误率的后处理系统:识别器输出投票错误减少(ROVER)
描述了一个由NIST开发的系统,当多个ASR系统的输出可用时,该系统可以产生复合自动语音识别(ASR)系统输出,并且在许多情况下,复合ASR输出的错误率低于任何单个系统。系统实现了“投票”或重新评分过程,以协调ASR系统输出的差异。我们把这个系统称为NIST识别器输出投票错误减少(ROVER)系统。随着额外的知识来源被添加到ASR系统中(例如声学和语言模型),错误率通常会降低。本文描述了一个后识别过程,该过程将多个ASR系统产生的输出建模为独立的知识来源,这些知识来源可以组合并用于产生具有较低错误率的输出。为了实现这一目标,通过动态规划(DP)对齐的迭代应用,将多个ASR系统的输出组合成一个最小成本的词转移网络(WTN)。生成的网络由自动评分或“投票”过程搜索,该过程选择得分最低的输出序列。
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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