Online training methods for Gaussian Mixture Vector Quantizers

This paper presents techniques relevant to the online training of Gaussian mixture vector quantizer (GMVQ) systems. techniques for learning from quantized data are considered, which enables online training configurations wherein the training is carried out remotely from the encoder. Next, methods for recursive training are presented, which eliminate the need to store large databases of example data, and also enable adaptive operation of the GMVQ system. These techniques are demonstrated on the problem of wideband speech spectrum quantization, and the performance losses due to the use of quantized training data are experimentally quantified as a function of the bit rate.