  This paper investigates several aspects of training a RNN (recurrent neural
network) that impact the objective and subjective quality of enhanced speech
for real-time single-channel speech enhancement. Specifically, we focus on a
RNN that enhances short-time speech spectra on a single-frame-in,
single-frame-out basis, a framework adopted by most classical signal processing
methods. We propose two novel mean-squared-error-based learning objectives that
enable separate control over the importance of speech distortion versus noise
reduction. The proposed loss functions are evaluated by widely accepted
objective quality and intelligibility measures and compared to other
competitive online methods. In addition, we study the impact of feature
normalization and varying batch sequence lengths on the objective quality of
enhanced speech. Finally, we show subjective ratings for the proposed approach
and a state-of-the-art real-time RNN-based method.
