  Human subjective evaluation is optimal to assess speech quality for human
perception. The recently introduced deep noise suppression mean opinion score
(DNSMOS) metric was shown to estimate human ratings with great accuracy. The
signal-to-distortion ratio (SDR) metric is widely used to evaluate
residual-echo suppression (RES) systems by estimating speech quality during
double-talk. However, since the SDR is affected by both speech distortion and
residual-echo presence, it does not correlate well with human ratings according
to the DNSMOS. To address that, we introduce two objective metrics to
separately quantify the desired-speech maintained level (DSML) and
residual-echo suppression level (RESL) during double-talk. These metrics are
evaluated using a deep learning-based RES-system with a tunable design
parameter. Using 280 hours of real and simulated recordings, we show that the
DSML and RESL correlate well with the DNSMOS with high generalization to
various setups. Also, we empirically investigate the relation between tuning
the RES-system design parameter and the DSML-RESL tradeoff it creates and offer
a practical design scheme for dynamic system requirements.
