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System and method to correct for packet loss using hidden markov models in ASR systems

机译:在ASR系统中使用隐马尔可夫模型校​​正分组丢失的系统和方法

摘要

A system and method are presented for the correction of packet loss in audio in automatic speech recognition (ASR) systems. Packet loss correction, as presented herein, occurs at the recognition stage without modifying any of the acoustic models generated during training. The behavior of the ASR engine in the absence of packet loss is thus not altered. To accomplish this, the actual input signal may be rectified, the recognition scores may be normalized to account for signal errors, and a best-estimate method using information from previous frames and acoustic models may be used to replace the noisy signal.
机译:提出了一种用于校正自动语音识别(ASR)系统中的音频中的分组丢失的系统和方法。如本文所呈现的,分组丢失校正发生在识别阶段,而不修改训练期间生成的任何声学模型。因此,在没有丢包的情况下,ASR引擎的行为不会改变。为此,可以校正实际的输入信号,可以对识别分数进行归一化以解决信号错误,并且可以使用使用来自先前帧的信息和声学模型的最佳估计方法来替换噪声信号。

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