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Lower WERs do not guarantee better transcriptions

机译:较低的WER不能保证更好的转录

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摘要

The goal of this paper is to investigate the effect of various properties of the CSR on automatic transcription. To this end, we used various versions of a continuous speech recognizer (CSR) to make automatic transcriptions. Our results show that changing certain properties of the CSR affects the resulting automatic transcriptions. The best results were obtained when 'short' hidden Markov models (HMMs), and context-independent HMMs were used. Furthermore, we found that minimizing the amount of contamination in the HMMs improves the quality of the automatic transcriptions. Another important result is that there does not appear to be a straightforward relation between word error rate (WER) and the transcription quality. In other words: A CSR with a lower WER does not always guarantee better transcriptions.
机译:本文的目的是研究CSR的各种特性对自动转录的影响。为此,我们使用了各种版本的连续语音识别器(CSR)进行自动转录。我们的结果表明,改变CSR的某些属性会影响自动转录的结果。当使用“短”隐马尔可夫模型(HMM)和上下文无关的HMM时,可获得最佳结果。此外,我们发现最小化HMM中的污染量可以提高自动转录的质量。另一个重要的结果是,单词错误率(WER)与转录质量之间似乎没有直接的关系。换句话说:具有较低WER的CSR并不总是保证更好的转录。

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