A new keyword spotting approach based on iterative dynamic programming
A new keyword spotting approach based on iterative dynamic programming
复制标题
一种基于迭代动态规划的关键词识别新方法
DOI：
10.1109/icassp.2000.862111
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
H. Bourlard
中科院分区：
文献类型：
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作者：
M. Silaghi;H. Bourlard
本文解决了在不显式建模非关键字片段的情况下，在无约束语音中检测关键字的问题。所提出的算法基于使用局部后验概率的置信度度量的最新发展，并在假设的关键字模型中沿着最可能的路径搜索最大平均后验观测值的段。众所周知，这种方法（有时称为滑动模型方法）需要放宽Viterbi匹配的起点/终点，以及对结果分数进行时间归一化，从而使动态规划变得次优或更复杂（更多的计算和/或更多的内存）。我们在这里提出了一个替代的（非常简单和有效的）解决方案，使用迭代形式的Viterbi解码算法，但它不需要对所有可能的起点/端点进行评分。该算法的收敛性证明是可用的（Silaghi和Bourlard， 1999）。本文报道了从BREF数据库中随机抽取的100个关键词的结果。
This paper addresses the problem of detecting keywords in unconstrained speech without explicit modeling of non-keyword segments. The proposed algorithm is based on recent developments in confidence measures using local posterior probabilities, and searches for the segment maximizing the average observation posteriori along the most likely path in the hypothesized keyword model. As known, this approach (sometimes referred to as sliding model method) requires a relaxation of the begin/endpoints of the Viterbi matching, as well as a time normalization of the resulting score, making dynamic programming sub-optimal or more complex (more computation and/or more memory). We present here an alternative (quite simple and efficient) solution to this problem, using an iterative form of Viterbi decoding algorithm, but which does not require scoring for all possible begin/endpoints. Convergence proof of this algorithm is available (Silaghi and Bourlard, 1999). Results obtained with this method on 100 keywords chosen at random from the BREF database are reported.