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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2011 >Individual Error Minimization Learning Framework and its Applications to Speech Recognition and Utterance Verification
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Individual Error Minimization Learning Framework and its Applications to Speech Recognition and Utterance Verification

机译：个体错误最小化学习框架及其在语音识别和话语验证中的应用

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摘要

In this paper, we extend the individual recognition error minimization criteria, MDE/MIE/MSE [1] in word-level and apply them to word recognition and verification tasks, respectively. In order to effectively reduce potential errors in word-level, we expand the training token selection scheme to be more appropriate for word-level learning framework, by taking into account neighboring words and by covering internal phonemes in each training word. Then, we examine the proposed word-level learning criteria on the TIMIT word recognition task and further investigate individual rejection performance of the recognition errors in utterance verification (UV). Experimental results confirm that each of the word-level objective criteria results in primarily reducing the corresponding target error type, respectively. The rejection rates of insertion and substitution errors are also improved within MIE and MSE criteria, which lead to additional word error rate reduction after the rejection.
机译：在本文中，我们在单词级别扩展了个体识别错误最小化标准MDE / MIE / MSE [1]，并将其分别应用于单词识别和验证任务。为了有效地减少单词级别的潜在错误，我们通过考虑相邻单词并覆盖每个训练单词的内部音素，来扩展训练令牌选择方案，使其更适合单词级别的学习框架。然后，我们检查了有关TIMIT单词识别任务的拟议单词级学习标准，并进一步研究了话语验证（UV）中识别错误的个体拒绝表现。实验结果证实，每个词级客观标准都会分别主要减少相应的目标错误类型。在MIE和MSE标准内，插入和替换错误的拒绝率也得到了改善，这导致拒绝后的字错误率进一步降低。

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