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Practical Implementations of Speaker-Adaptive Training

机译：演讲者适应培训的实用实施

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Speaker Adaptive Training (SAT) has been shown to achieve significant word error reductions relative to the common Speaker Independent (SI) training paradigm, but its high requirements in disk I/O and space make it impractical for training on more than a couple hundred speakers. In the 1996 Hub-4 evaluation, the 38 hours of broadcast news training data consist of approximately 2000 speakers, half of them having less than 20 seconds of speech. In this paper we propose three implementations of SAT that are practical for training sets with a few thousands of speakers. First we present a two-pass SAT procedure that is mathematically equivalent to the original SAT method, with significantly reduced requirements in disk space, but essentially double the training time. Then we describe the Inverse Transform SAT (ITSAT) and the Least Squares SAT (LSSAT), two approximations to the SAT parameter estimation with time and space requirements that match those of common SI training. We show that the ITSAT method suffers only 1% degradation relative to the original SAT method.
机译：扬声器自适应培训（SAT）已被证明可以实现相对于公共扬声器独立（SI）培训范式的重要词汇销售，但它在磁盘I / O和空间中的高要求使其对培训进行了不切实际的培训，而不是几百个扬声器。在1996年的HUB-4评估中，38小时的广播新闻培训数据由大约2000名扬声器组成，其中一半的言论少于20秒。在本文中，我们提出了三个坐限的实施，这对于培训套装具有几千名扬声器的培训。首先，我们介绍了一个双通的SAT程序，它在数学上等同于原始SAT方法，在磁盘空间中的要求显着降低，但基本上是训练时间的两倍。然后，我们描述了逆变换SAT（ITSAT）和最小二乘SAT（LSSAT），与SAT参数估计的两个近似与与常见的SI训练相匹配的时间和空间要求。我们表明ITSAT方法相对于原始SAT方法仅损害1％。

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