Dynamic programming based segmentation approach to LSF matrix reconstruction

We propose a methodology of speech segmentation in which the LSF feature vector matrix of a segment is reconstructed optimally using a set of parametric/non-parametric functions. We have explored approximations using basis functions or polynomials. We have analyzed the performance of these methods w.r.t. phoneme segmentation (on 100 TIMIT sentences) and reconstruction error based on spectral distortion (SD) measure. We study how amenable these methods are to quantization and their suitability for speech coding. We also estimate the optimum number of segments depending on the reconstruction performance achieved using that many number of segments and the tolerance limit set on the spectral distortion error.