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Statistical Approaches to Excitation Modeling in HMM-Based Speech Synthesis

机译:基于HMM的语音合成中激励建模的统计方法

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In our previous study, we proposed the waveform interpolation (WI) approach to model the excitation signals for hidden Markov model (HMM)-based speech synthesis. This letter presents several techniques to improve excitation modeling within the WI framework. We propose both the time domain and frequency domain zero padding techniques to reduce the spectral distortion inherent in the synthesized excitation signal. Furthermore, we apply non-negative matrix factorization (NMF) to obtain a low-dimensional representation of the excitation signals. From a number of experiments, including a subjective listening test, the proposed method has been found to enhance the performance of the conventional excitation modeling techniques.
机译:在我们先前的研究中,我们提出了波形插值(WI)方法来对基于隐马尔可夫模型(HMM)的语音合成中的激励信号进行建模。这封信提出了几种在WI框架内改善激励建模的技术。我们提出时域和频域零填充技术,以减少合成激励信号中固有的频谱失真。此外,我们应用非负矩阵分解(NMF)以获得激励信号的低维表示。从包括主观听觉测试在内的许多实验中,已发现所提出的方法可以增强常规激励建模技术的性能。

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