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This paper investigates several aspects of training a RNN (recurrent neuralnetwork) that impact the objective and subjective quality of enhanced speechfor real-time single-channel speech enhancement. Specifically, we focus on aRNN that enhances short-time speech spectra on a single-frame-in,single-frame-out basis, a framework adopted by most classical signal processingmethods. We propose two novel mean-squared-error-based learning objectives thatenable separate control over the importance of speech distortion versus noisereduction. The proposed loss functions are evaluated by widely acceptedobjective quality and intelligibility measures and compared to othercompetitive online methods. In addition, we study the impact of featurenormalization and varying batch sequence lengths on the objective quality ofenhanced speech. Finally, we show subjective ratings for the proposed approachand a state-of-the-art real-time RNN-based method.

Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement