  Good speech quality has been achieved using waveform matching and parametric
reconstruction coders. Recently developed very low bit rate generative codecs
can reconstruct high quality wideband speech with bit streams less than 3 kb/s.
These codecs use a DNN with parametric input to synthesise high quality speech
outputs. Existing objective speech quality models (e.g., POLQA, ViSQOL) do not
accurately predict the quality of coded speech from these generative models
underestimating quality due to signal differences not highlighted in subjective
listening tests. We present WARP-Q, a full-reference objective speech quality
metric that uses dynamic time warping cost for MFCC speech representations. It
is robust to small perceptual signal changes. Evaluation using waveform
matching, parametric and generative neural vocoder based codecs as well as
channel and environmental noise shows that WARP-Q has better correlation and
codec quality ranking for novel codecs compared to traditional metrics in
addition to versatility for general quality assessment scenarios.
