  In this work, we propose a frequency bin-wise method to estimate the
single-channel speech presence probability (SPP) with multiple deep neural
networks (DNNs) in the short-time Fourier transform domain. Since all frequency
bins are typically considered simultaneously as input features for conventional
DNN-based SPP estimators, high model complexity is inevitable. To reduce the
model complexity and the requirements on the training data, we take a single
frequency bin and some of its neighboring frequency bins into account to train
separate gate recurrent units. In addition, the noisy speech and the a
posteriori probability SPP representation are used to train our model. The
experiments were performed on the Deep Noise Suppression challenge dataset. The
experimental results show that the speech detection accuracy can be improved
when we employ the frequency bin-wise model. Finally, we also demonstrate that
our proposed method outperforms most of the state-of-the-art SPP estimation
methods in terms of speech detection accuracy and model complexity.
