加权有限状态传感器的快速合成算法

J. McDonough, Emilian Stoimenov, D. Klakow
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引用次数: 20

摘要

在基于加权有限换能器的自动语音识别中,通常构造静态解码图HC ~ L ~ G。在这项工作中,首先展示如何解码图像的大小可以减少和determinizing可以被删除的必要性与过渡到相关的模糊补偿国家或州g .然后我们展示静态结构可以避免完全由执行快速动态组成HC和L o g .我们证明了基于语音识别的动态组成大约80%比识别基于statically-expanded网络运行时R,这使得它与文献中出现的其他动态展开算法相比具有竞争力。此外,基于静态解码图的识别,动态算法所需的主存储器大约减少了7倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An algorithm for fast composition of weighted finite-state transducers
In automatic speech recognition based on weighted-finite transducers, a static decoding graph HC o L o G is typically constructed. In this work, we first show how the size of the decoding graph can be reduced and the necessity of determinizing it can be eliminated by removing the ambiguity associated with transitions to the backoff state or states in G. We then show how the static construction can be avoided entirely by performing fast on-the-fly composition of HC and L o G. We demonstrate that speech recognition based on this on-the-fly composition approximately 80% more run-time than recognition based on the statically-expanded network R, which makes it competitive compared with other dynamic expansion algorithms that have appeared in the literature. Moreover, the dynamic algorithm requires a factor of approximately seven less main memory as the recognition based on the static decoding graph.
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