提出基于受限玻尔兹曼机的频谱建模与单元挑选语音合成方法。在模型训练阶段，采用受限玻尔兹曼机对包含丰富细节的频谱特征建模，如谱包络、短时幅度谱，取代传统的使用对角方差单高斯模型和梅尔倒谱特征的频谱建模方法，改善声学模型对于频谱特征的描述能力。在语音合成阶段，使用训练得到的受限玻尔兹曼机模型计算备选样本频谱特征的对数似然值，并通过分段线性映射构建单元挑选的目标代价函数。实验表明文中方法可有效提高合成语音的自然度。%A restricted Boltzmann machine based spectrum modeling and unit selection speech synthesis method is proposed. At the model training stage, the restricted Boltzmann machine is used to model spectral features with rich details, such as spectral envelopes and short﹣time spectral amplitudes, instead of using the single Gaussian model with diagonal variance and mel﹣cepstrum feature for spectral model in the traditional approach. Thus, the description capability of the acoustical model for spectral feature is improved. At the speech synthesis stage, the restricted Boltzmann machine model is adopted to calculate the log likelihoods of spectral feature of candidate sample, and a method of piecewise linear mapping is proposed to construct target cost function for unit selection. The experimental results indicate that the proposed method can effectively improve the naturalness of synthetic speech.
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