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Utterance verification On DTW based speech recognition using likelihood

机译：基于DTW的语音识别使用可能性的话语验证

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Utterance verification provides the speech recognition system a user-friendly interaction. However, small-vocabulary system using DTW algorithm cannot afford HMM based utterance verification. So, to equip the DTW based recognizer with effective utterance verification becomes an essential problem in a low computational application. We proposed a new utterance verification method, combining both statistic and distance measure capability, to map DTW distance to a certain likelihood. The likelihood as a confidence measure performs a good ability of both speech recognition and utterance verification. With a test set of fifteen words, at 5.24% false rejection, the verification method brought on 10.44% false alarm rate and 93.61% accuracy. Furthermore, 94.76% out-of-vocabulary utterances were correctly rejected.
机译：话语验证为语音识别系统提供了一个用户友好的交互。然而，使用DTW算法的小词汇系统不能承担基于赫姆的话语验证。因此，为了装备基于DTW的识别器，具有有效的话语验证成为低计算应用中的重要问题。我们提出了一种新的话语验证方法，结合统计和距离测量能力，将DTW距离映射到某个可能性。置信度量的可能性表现出语音识别和话语验证的良好能力。使用五十个单词的测试集，在5.24％的假抑制下，验证方法带来了10.44％的误报率和93.61％的准确度。此外，94.76％的词汇话语被正确拒绝。

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