Parameter optimization for vocal tract length normalization

This paper focuses on the optimization of model parameters for vocal tract length normalization (VTLN). For maximum likelihood (ML) based normalization techniques, the complexity of the VTL-models is a source of variation in system performance. An optimal complexity for the VTL-model that ensures best global word error rate is proposed. The choice of frequency warping factor also depends on the signal processing step of VTLN. A best set of parameters for the VTLN signal processing stage is proposed with extensive results for an optimal frequency range.