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Transform domain vector quantization for speech signals

This paper presents a new approach to efficient vector quantization. The proposed Transform Domain Vector Quantization (TDVQ) system consists of a two-stage coding scheme: A Karhunen-Loewe Transform stage through which minimal representation of the source is achieved followed by a feature extraction based Vector Quantizer (VQ). Based on the compact structure of the source in its eigenspace, two different non-related techniques are employed to circumvent ccmplexity in VQ's: an optimal fast search algorithm which substantially alleviates the computational complexity of encoding correlated sources and a sub-optimal partial representation technique which reduces both memory and computational requirements. The complexity and the performance of the TDVQ system are presented both for an highly correlated first order Gauss-Markov source and for speech waveforms, confirming its advantage over the traditionally used time domain VQ.