Speaker-adapted training on the Switchboard Corpus
Speaker-adapted training on the Switchboard Corpus
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在总机语料库上进行针对扬声器的训练
DOI：
10.1109/icassp.1997.596123
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发表时间：
1997
期刊：
影响因子：
--
通讯作者：
H. Gish
中科院分区：
文献类型：
--
作者：
J. McDonough;T. Anastasakos;G. Zavaliagkos;H. Gish
说话人自适应是指将某些与说话人无关的声学模型转换为更接近特定说话人特征的过程。它已被一些研究人员证明是提高大词汇量连续语音识别系统性能的有效手段。直到最近，说话人自适应一直被专门用作识别过程的一部分。这是不期望的，因为它导致测试和训练之间的不匹配条件，并且因此导致次优识别性能。将说话人自适应技术应用于HMM训练以缓解训练/测试不匹配的问题已经引起了越来越多的关注。在以前的工作中，我们提出了一个迭代方案，用于确定最大似然解决方案的一组说话者独立的手段和方差时，说话者相关的自适应HMM训练过程中进行。在目前的工作中，我们调查遇到的具体问题，在应用这个一般框架的任务，提高识别性能的开关板语料库。
Speaker adaptation is the process of transforming some speaker-independent acoustic model in such a way as to more closely match the characteristics of a particular speaker. It has been shown by several researchers to be an effective means of improving the performance of large vocabulary continuous speech recognition systems. Until very recently speaker adaptation has been used exclusively as a part of the recognition process. This is undesirable inasmuch as it leads to a mismatched condition between test and training, and hence sub-optimal recognition performance. There has been a growing interest in applying speaker-adaptation techniques to HMM training in order to alleviate the training/test mismatch. In prior work, we presented an iterative scheme for determining the maximum likelihood solution for the set of speaker-independent means and variances when speaker-dependent adaptation is performed during HMM training. In the present work, we investigate specific issues encountered in applying this general framework to the task of improving recognition performance on the Switchboard Corpus.