基于声调核参数及DNN建模的韵律边界检测研究

韵律边界对言语表达的自然度和可理解度有着重要作用。韵律建模也是语音合成、语音理解中的重要方面。该文从相邻声调的相互作用角度出发,提出基于深度神经网络(DNN)及声调核声学特征的汉语韵律边界检测方法。该方法首先采用声调核部分的声学特征来计算边界检测相关参数。然后,利用深度神经网络进行建模。作为对比,实验中采用了以整个音节的声学特征为输入特征的基线系统。结果表明,只使用调核部分声学特征的系统优于使用整个音节的系统,韵律边界检测正确率相对提高了4%,这表明该文提出的汉语韵律边界检测方法的有效性。

Automatic Mandarin Prosody Boundary Detection Based on Tone Nucleus and DNN Model

Prosody boundary plays an important role in naturalness and intelligibility of verbal expressions. Thus, prosody modeling is also an important aspect of speech synthesis and understanding. Focused on the interaction of adjacent tones, we propose a method of prosody boundary detection based on tone nucleus and DNN model. This method calculates the boundary-related parameters by applying the tone nucleus features. Then, the parameters are modeled by the deep neural network. For comparison, the baseline system chooses syllable the acoustic feature. The experimental results show a relative 4% improvement achieved by the proposed method.