Abstract
This article investigates the possibility to use the class entropy of the output of a connectionist phoneme recogniser to predict time boundaries between phonetic classes. The rationale is that the value of the entropy should increase in proximity of a transition between two segments that are well modelled (known) by the recognition network since it is a measure of uncertainty. The advantage of this measure is its simplicity as the posterior probabilities of each class are available in connectionist phoneme recognition. The entropy and a number of measures based on differentiation of the entropy are used in isolation and in combination. The decision methods for predicting the boundaries range from simple thresholds to neural network based procedure. The different methods are compared with respect to their precision, measured in terms of the ratio between the number C of predicted boundaries within 10 or 20 msec of the reference and the total number of predicted boundaries, and recall, measured as the ratio between C and the total number of reference boundaries.
Abstract (translated)
这篇文章研究了使用连接ist语音音素识别器输出的类熵来预测音位类之间的时间边界的可能性。推理是，熵的值应该在两个已经很好地建模（已知）的音位类之间的转换处增加，因为这是不确定性的度量。这种度量的优势在于，后验概率可以在连接ist语音素识别中直接访问。熵及其基于导数的度量被单独和结合使用。预测边界的方法从简单的阈值开始，到基于神经网络的程序。本文将不同的方法与它们的精度进行比较，即预测边界内参考边界数量与总预测边界数量之比。召回被测量为预测边界内参考边界数量与总预测边界数量之比。
URL
https://arxiv.org/abs/2401.05717
