A new waveform interpolation coding scheme based on pitch synchronous wavelet transform decomposition

This correspondence uses a pitch synchronous wavelet transform (PSWT) as an alternative characteristic waveform decomposition method for the waveform interpolation (WI) paradigm. The proposed method has the benefit of providing additional scalability in quantization than the existing WI decomposition to meet desired quality requirements. The PSWT is implemented as a quadrature mirror filter bank and decomposes the characteristic waveform surface into a series of reduced time resolution surfaces. Efficient quantization of these surfaces is achieved by exploiting their perceptual importance and inherent transmission rate requirements. The multiresolution representation has the additional benefit of more flexible parameter quantization, allowing a more accurate description of perceptually important scales, especially at higher coding rates. The proposed PSWT-WI coder is very well suited to high quality speech storage applications

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research