In this paper, we present a comprehensive evaluation of five quantization techniques for variable-dimension spectral vectors in a waveform interpolation speech coder. Each technique included in the evaluation is based on dimension conversions. The conversions are performed using zero-pad and truncation, frequency bins, band-limited interpolation, discrete cosine transform, and polynomial approximation. In addition to assessing quantization accuracy, this study considers the complexity of the techniques. The evaluation indicates that the selection of the optimal quantization technique is a trade-off between coding accuracy, complexity, and memory requirements. According to our results, the technique based on discrete cosine transform appears to be a strong candidate for many applications.