Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods
Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods
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发表时间：
2010-08
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通讯作者：
C. Evers;J. Hopgood
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作者：
C. Evers;J. Hopgood
室内混响导致音频信号清晰度降低。因此，增强对于高质量的音频和场景分析应用程序至关重要。本文提出了从失真观测中直接最优估计源信号和声信道的方法。剩余的模型参数从粒子滤波器中采样，便于实时去噪。该方法已成功应用于单传感器和多传感器盲消噪。通过对语音产生系统进行精确建模，可以进一步提高增强效果。因此，本文扩展了盲去噪方法，纳入了一种基于并行形成峰合成的新型源模型，并将该方法与使用时变AR模型的方法进行了比较，该模型的参数根据随机游走而变化。实验数据表明，该模型对元音、停辅音和摩擦音的去音效果有明显改善。
Room reverberation leads to reduced intelligibility of audio signals. Enhancement is thus crucial for high-quality audio and scene analysis applications. This paper proposes to directly and optimally estimate the source signal and acoustic channel from the distorted observations. The remaining model parameters are sampled from a particle filter, facilitating real-time dereverberation. The approach was previously successfully applied to single- and multisensor blind dereverberation. Enhancement can be improved upon by accurately modelling the speech production system. This paper therefore extends the blind dereverberation approach to incorporate a novel source model based on parallel formant synthesis and compares the approach to one using a time-varying AR model, with parameters varying according to a random walk. Experimental data shows that dereverberation using the proposed model is improved for vowels, stop consonants, and fricatives.