A Novel Method for Accurate Analysis and Modeling of Voice Source Waveforms EMIR
Analysis and modeling of voice source waveforms are some of the most challenging fields of speech processing owing to the fact that voice source signals commonly exhibit complex temporal morphology and contain numerous artifacts of data collection process. In this paper, we have proposed a novel and a fully automatic source-filter based framework for voice source parameterization, modelling and synthesis. The proposed method is not constrained to the idealized glottal waveform approximations, but instead relies on the observed signal to ascertain a non-deterministic and adaptable model for the voice source signal. The proposed signal synthesis algorithm is able to independently account for the temporal and spatial dynamics of consecutive voice source pulses. In comparative evaluation with the popular Liljencrants-Fant’s model, it was found that the proposed method has the capacity to represent complex voice source features that cannot be accurately or efficiently represented by a deterministic model. It is demonstrated that the proposed method offers high levels of robustness and accuracy in signal parameterization and reconstruction. Key-Words: Biomedical Signal Processing; Digital Speech Processing; Glottal Flow Modeling