Title: Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications,
Abstract: Mean square error (MSE) has been the preferred choice as loss function in the
current deep neural network (DNN) based speech separation techniques. In this
paper, we propose a new cost function with the aim of optimizing the extended
short time objective intelligibility (ESTOI) measure. We focus on applications
where low algorithmic latency ($\leq 10$ ms) is important. We use long
short-term memory networks (LSTM) and evaluate our proposed approach on four
sets of two-speaker mixtures from extended Danish hearing in noise (HINT)
dataset. We show that the proposed loss function can offer improved or at par
objective intelligibility (in terms of ESTOI) compared to an MSE optimized
baseline while resulting in lower objective separation performance (in terms of
the source to distortion ratio (SDR)). We then proceed to propose an approach
where the network is first initialized with weights optimized for MSE criterion
and then trained with the proposed ESTOI loss criterion. This approach
mitigates some of the losses in objective separation performance while
preserving the gains in objective intelligibility.