A hybrid harmonics-and-bursts modelling approach to speech synthesis

J. Beskow, Harald Berthelsen
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引用次数: 1

Abstract

Statistical speech synthesis systems rely on a parametric speech generation model, typically some sort of vocoder. Vocoders are great for voiced speech because they offer independent control over voice source (e.g. pitch) and vocal tract filter (e.g. vowel quality) through control parameters that typically vary smoothly in time and lend themselves well to statistical modelling. Voiceless sounds and transients such as plosives and fricatives on the other hand exhibit fundamentally different spectro-temporal behaviour. Here the benefits of the vocoder are not as clear. In this paper, we investigate a hybrid approach to modeling the speech signal, where speech is decomposed into an harmonic part and a noise burst part through spectrogram kernel filtering. The harmonic part is modeled using vocoder and statistical parameter generation, while the burst part is modeled by concatenation. The two channels are then mixed together to form the final synthesized waveform. The proposed method was compared against a state of the art statistical speech synthesis system (HTS 2.3) in a perceptual evaluation, which reveled that the harmonics plus bursts method was perceived as significantly more natural than the purely statistical variant.
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语音合成的混合谐波和爆发建模方法
统计语音合成系统依赖于参数语音生成模型,通常是某种声码器。声码器对于浊音来说非常有用,因为它们通过控制参数提供对声源(如音高)和声道滤波器(如元音质量)的独立控制,这些参数通常随时间平滑变化,并且可以很好地用于统计建模。另一方面,无声音和瞬变音,如爆破音和摩擦音,则表现出根本不同的光谱-时间行为。在这里声码器的好处不是很清楚。本文研究了一种混合建模语音信号的方法,该方法通过谱图核滤波将语音分解为谐波部分和噪声突发部分。谐波部分采用声码器和统计参数生成建模,突发部分采用串接建模。然后将两个通道混合在一起形成最终的合成波形。在感知评估中,将所提出的方法与最先进的统计语音合成系统(HTS 2.3)进行了比较,结果表明谐波加突发方法被认为比纯粹的统计变体更自然。
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