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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2010 >On-the-fly Lattice Rescoring for Real-time Automatic Speech Recognition
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On-the-fly Lattice Rescoring for Real-time Automatic Speech Recognition

机译：实时格点记录实时自动语音识别

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摘要

This paper presents a method for rescoring the speech recognition lattices on-the-fly to increase the word accuracy while preserving low latency of a real-time speech recognition system. In large vocabulary speech recognition systems, pruned and/or lower order n-gram language models are often used in the first-pass of the speech decoder due to the computational complexity. The output word lattices are rescored offline with a better language model to improve the accuracy. For real-time speech recognition systems, offline lattice rescoring increases the latency of the system and may not be appropriate. We propose a method for on-the-fly lattice rescoring and generation, and evaluate it on a broadcast speech recognition task. This first-pass lattice rescoring method can generate rescored lattices with less than 20% increased computation over standard lattice generation without increasing the latency of the system.
机译：本文提出了一种实时记录语音识别格点的方法，可以在保持实时语音识别系统的低延迟的同时，提高单词的准确性。在大词汇量语音识别系统中，由于计算复杂性，通常在语音解码器的首遍中使用修剪和/或低阶n-gram语言模型。使用更好的语言模型对输出的单词晶格进行离线重新记录，以提高准确性。对于实时语音识别系统，离线晶格记录会增加系统的延迟，因此可能不合适。我们提出了一种实时格记录和生成的方法，并在广播语音识别任务上对其进行了评估。这种首过点阵记录方法可以在不增加系统等待时间的情况下，以比标准点阵生成少20％的计算量来生成重新记录的点阵。

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