  Reverberation, especially in large rooms, severely degrades speech
recognition performance and speech intelligibility. Since direct measurement of
room characteristics is usually not possible, blind estimation of
reverberation-related metrics such as the reverberation time (RT) and the
direct-to-reverberant energy ratio (DRR) can be valuable information to speech
recognition and enhancement algorithms operating in enclosed environments. The
objective of this work is to evaluate the performance of five variants of blind
RT and DRR estimators based on a modulation spectrum representation of
reverberant speech with single- and multi-channel speech data. These models are
all based on variants of the so-called Speech-to-Reverberation Modulation
Energy Ratio (SRMR). We show that these measures outperform a state-of-the-art
baseline based on maximum-likelihood estimation of sound decay rates in terms
of root-mean square error (RMSE), as well as Pearson correlation. Compared to
the baseline, the best proposed measure, called NSRMR_k , achieves a 23%
relative improvement in terms of RMSE and allows for relative correlation
improvements ranging from 13% to 47% for RT prediction.
