This paper considers an assessment and evaluation of speech sound pronunciation quality in computer-aided language learning systems. We examine the gain optimization of spectral distortion measures between the speech signals of a native speaker and a learner. During training, a learner has to achieve stable pronunciation of all sounds. This is measured by computing the distances between the sounds produced by the learner and the model speaker. In order to improve pronunciation, it is proposed to adapt the linear prediction coding coefficients of reference sounds by using the gradient descent optimization of the gain-optimized dissimilarity. As a result, we demonstrate the possibility of synthesizing sounds that will be either close to the model pronunciation or achievable by a learner. An experimental study shows that the proposed procedure leads to high efficiency for pronunciation training even in the presence of noise in the observed utterance.

本文考虑了计算机辅助语言学习系统中语音发音质量的评估和评价。我们研究了母语者和学习者的语音信号之间频谱失真测量的增益优化。在训练期间，学习者必须实现所有声音的稳定发音。这是通过计算学习者和模型说话者发出的声音之间的距离来衡量的。为了改善发音，提出通过使用增益优化的相异度的梯度下降优化来适应参考声音的线性预测编码系数。因此，我们展示了合成接近模型发音或学习者可以实现的声音的可能性。