Boosting the performance of connectionist large vocabulary speech recognition
Boosting the performance of connectionist large vocabulary speech recognition
复制标题
提升联结主义大词汇量语音识别的性能
DOI：
10.1109/icslp.1996.607852
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发表时间：
1996
期刊：
影响因子：
--
通讯作者：
A. J. Robinson
中科院分区：
文献类型：
--
作者：
G. Cook;A. J. Robinson
连接-隐马尔可夫混合模型大词汇量语音识别已经被证明是与更传统的HMM系统相竞争的。连接主义声学模型通常使用比HMM少得多的参数，允许实时操作而不会显著降低性能。然而，连接主义声学模型中的少量参数也带来了一个问题--如何最大限度地利用大量的训练数据？本文针对这一问题提出了一种解决方案，在该方案中，“智能”过程选择性地使用训练数据来提高性能。
Hybrid connectionist-hidden Markov model large vocabulary speech recognition has been shown to be competitive with more traditional HMM systems. Connectionist acoustic models generally use considerably less parameters than HMM's, allowing real-time operation without significant degradation of performance. However, the small number of parameters in connectionist acoustic models also poses a problem-how do we make the best use of large amounts of training data? This paper proposes a solution to this problem in which a "smart" procedure makes selective use of training data to increase performance.