Analysis and Synthesis of Speech Using an Adaptive Full-Band Harmonic Model
Analysis and Synthesis of Speech Using an Adaptive Full-Band Harmonic Model
复制标题
使用自适应全带谐波模型进行语音分析和合成
DOI：
10.1109/tasl.2013.2266772
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Y. Stylianou
中科院分区：
文献类型：
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作者：
G. Degottex;Y. Stylianou
语音模型通常使用频率限制将语音频谱划分为两个或多个浊音/非浊音频段。但从声音产生来看，浊音源的幅度谱平滑下降，没有突兀的频率限制。因此，多波段模型难以估计这些限制，因此，伪像会降低感知质量。使用适应语音信号非平稳性的线性频率基，风扇啁啾变换（FChT）在比DFT通常观察到的频率更高的频率下显示出谐波，这激发了全频带建模。先前提出的自适应准谐波模型（aQHM）通过使用非线性频率基，比FChT具有更大的灵活性。在本文中，我们利用aQHM的特性，描述了一种全频段自适应谐波模型（aHM），并详细描述了其相应的算法，用于估计奈奎斯特频率以下的谐波。正式的听力测试表明，使用aHM重建的语音与原始语音几乎没有区别。对合成信号的实验也表明，所提出的aHM在估计正弦参数的精度方面总体上优于先前的正弦和谐波模型。从一个角度来看，这样的精度对于在正弦参数上建立更高层次的模型是很有趣的，比如语音合成的频谱包络。
Voice models often use frequency limits to split the speech spectrum into two or more voiced/unvoiced frequency bands. However, from the voice production, the amplitude spectrum of the voiced source decreases smoothly without any abrupt frequency limit. Accordingly, multiband models struggle to estimate these limits and, as a consequence, artifacts can degrade the perceived quality. Using a linear frequency basis adapted to the non-stationarities of the speech signal, the Fan Chirp Transformation (FChT) have demonstrated harmonicity at frequencies higher than usually observed from the DFT which motivates a full-band modeling. The previously proposed Adaptive Quasi-Harmonic model (aQHM) offers even more flexibility than the FChT by using a non-linear frequency basis. In the current paper, exploiting the properties of aQHM, we describe a full-band Adaptive Harmonic Model (aHM) along with detailed descriptions of its corresponding algorithms for the estimation of harmonics up to the Nyquist frequency. Formal listening tests show that the speech reconstructed using aHM is nearly indistinguishable from the original speech. Experiments with synthetic signals also show that the proposed aHM globally outperforms previous sinusoidal and harmonic models in terms of precision in estimating the sinusoidal parameters. As a perspective, such a precision is interesting for building higher level models upon the sinusoidal parameters, like spectral envelopes for speech synthesis.