CAJ | 학술논문

提出了一种基于随机段模型的发音信息集成方法。根据随机段模型的模型特性，建立了阶层式人工神经网络来获取语音段信号属于各类音素的后验概率，并通过一遍解码的方式集成到随机段模型系统中。在“863-test”测试集上进行的汉语连续语音识别实验显示汉语字的相对错误率下降了5．93％。实验结果表明了将发音信息应用到随机段模型的可行性。
제출료일충기우수궤단모형적발음신식집성방법。근거수궤단모형적모형특성，건립료계층식인공신경망락래획취어음단신호속우각류음소적후험개솔，병통과일편해마적방식집성도수궤단모형계통중。재“863-test”측시집상진행적한어련속어음식별실험현시한어자적상대착오솔하강료5．93％。실험결과표명료장발음신식응용도수궤단모형적가행성。
This paper proposed a framework which attempted to incorporate articulatory information into the stochastic segment model based on Mandarin speech recognition system.According to the characteristics of the stochastic segment model,it used hierarchical artificial neural network to obtain the posterior probability of speech signal belonging to the phonemes.Then,it integrated the posterior probability into the stochastic segment model system in the first search process.Experiments conducted on “863-test”set show that about 5 .93% relative improvement could be achieved in the recognition accuracy.Thus,it de-monstrates the feasibility of the method.