  This study compares the performances of different algorithms for coding
speech at low bit rates. In addition to widely deployed traditional vocoders, a
selection of recently developed generative-model-based coders at different bit
rates are contrasted. Performance analysis of the coded speech is evaluated for
different quality aspects: accuracy of pitch periods estimation, the word error
rates for automatic speech recognition, and the influence of speaker gender and
coding delays. A number of performance metrics of speech samples taken from a
publicly available database were compared with subjective scores. Results from
subjective quality assessment do not correlate well with existing full
reference speech quality metrics. The results provide valuable insights into
aspects of the speech signal that will be used to develop a novel metric to
accurately predict speech quality from generative-model-based coders.
