- Mads Græsbøll Christensen, Aalborg University
- Assistant Prof. Jesper Rindom Jensen, Aalborg University
- Assistant Prof. Jesper Kjær Nielsen, Aalborg University.
Parametric speech models have been around for many years but have always had their detractors. Two common arguments against such models are that it is too difficult to find their parameters and that the models do not take the complicated nature of real signals into account. In recent years, significant advances have been made in speech models and robust and computationally efficient estimation using statistical principles, and it has been demonstrated that, regardless of any deficiencies in the model, the parametric methods outperform the more commonly used non-parametric methods (e.g., autocorrelation-based methods) for problems like pitch estimation. The application of these principles, however, extend way beyond that problem. In this tutorial, state-of-the-art parametric speech models and statistical estimators for finding their parameters will be presented and their pros and cons discussed. The merits of the statistical, parametric approach to speech modeling will be demonstrated via a number of number of well-known problems in speech, audio and acoustic signal processing. Examples of such problems are pitch estimation for non-stationary speech, distortion-less speech enhancement, noise statistics estimation, speech segmentation, multi-channel modeling, and model-based localization and beamforming with microphone arrays.