Estimation of glottal source waveforms and vocal tract shapes from speech signals based on ARX-LF model
Yongwei Li, Ken‐Ichi Sakakibara, Masato Akagi
Abstract
Open-access reader
Yongwei Li, Ken‐Ichi Sakakibara, Masato Akagi
Abstract
Open-access reader
The widely used method to estimate glottal source waveform and vocal tract shape is to process speech signal using inverse filter and then to fit residual signal using glottal source model. However, since source-tract interactions, estimation accuracy is reduced. In this paper, we propose a method to estimate glottal source waveform and vocal tract shape simultaneously based on analysis-by-synthesis approach with a source-filter model constructed with an auto-regressive eXogenous (ARX) model combined with the Lilijencrant-Fant (LF) model. Since the optimization of multiple parameters makes simultaneous estimation difficult, there are two steps: the glottal source parameters are initialized using the inverse filter method, then the accurate parameters of the glottal source and the vocal tract shape are estimated simultaneously using an analysis-by-synthesis approach. Experimental results with synthetic and real speech signals showed the higher estimation accuracy of the proposed method than inverse filter.
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The widely used method to estimate glottal source waveform and vocal tract shape is to process speech signal using inverse filter and then to fit residual signal using glottal source model. However, since source-tract interactions, estimation accuracy is reduced. In this paper, we propose a method to estimate glottal source waveform and vocal tract shape simultaneously based on analysis-by-synthesis approach with a source-filter model constructed with an auto-regressive eXogenous (ARX) model combined with the Lilijencrant-Fant (LF) model. Since the optimization of multiple parameters makes simultaneous estimation difficult, there are two steps: the glottal source parameters are initialized using the inverse filter method, then the accurate parameters of the glottal source and the vocal tract shape are estimated simultaneously using an analysis-by-synthesis approach. Experimental results with synthetic and real speech signals showed the higher estimation accuracy of the proposed method than inverse filter.
Key concepts: Vocal tract, Inverse filter, Waveform, Computer science, Filter (signal processing), Speech recognition, SIGNAL (programming language), Speech synthesis