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首页> 外文会议>WSEAS international conference on computers;WSEAS CSCC multiconference >On the Use of Higher Frame Rate in the Training Phase of ASR
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On the Use of Higher Frame Rate in the Training Phase of ASR

机译：关于在ASR训练阶段使用更高的帧速率

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摘要

The number of observations which are the basis for parameter estimation plays an important role in the quality of acoustic models. HMM based automatic speech recognition (ASR) systems generally have to cope with an insufficient number of observations for a good estimate. One way of tackling this problem is a well known procedure of state-tying, which is performed in order to gather sufficient information for a reasonable estimate for a large number of models. This procedure introduces an additional bias into the estimates, often leading to poor recognition results. In this paper a simple alternative to that solution is offered. It should be noted that most existing ASR systems use the same frame step size of 10ms in the training of the acoustical models, justifying it with the fact that speech signals exhibit quasi-stationary behavior at shorter durations. We claim that it is fully acceptable to adopt a much smaller frame step size in the acoustical training, thus providing estimators with a significantly higher number of observations compared to the standard 10ms case. This results in better parameter estimates and consequently better recognition results. Beside being justifiable from a phonetical point of view, it is also supported by results of an experimental on a real ASR system.
机译：作为参数估计基础的观察次数在声学模型的质量中起着重要作用。基于HMM的自动语音识别（ASR）系统通常必须应对数量不足的观察结果才能获得良好的估计。解决该问题的一种方法是众所周知的状态绑定过程，该过程旨在收集足够的信息以对大量模型进行合理的估计。此过程在估计中引入了额外的偏差，通常会导致较差的识别结果。在本文中，提供了该解决方案的简单替代方案。应该注意的是，大多数现有的ASR系统在声学模型的训练中都使用10ms的相同帧步长，这是事实，即语音信号在较短的持续时间内表现出准平稳的行为。我们声称在声学训练中采用小得多的帧步长是完全可以接受的，因此与标准的10ms情况相比，为估计量提供了更高的观测值。这导致更好的参数估计，因此得到更好的识别结果。除了从语音的角度来看是合理的，它还得到了在真实ASR系统上进行的实验结果的支持。

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