Partial Monotonic Speech Quality Estimation in ViSQOL with Deep Lattice Networks

When predicting subjective quality as mean opinion score (MOS) for speech, a raw similarity score is often mapped onto the score dimension with a mapping function. Virtual Speech Quality Objective Listener (ViSQOL) uses monotonic one-dimensional mappings to evaluate speech. More recent models such as support vector regression (SVR) or deep neural networks (DNNs) use multidimensional input, which allows for a more accurate prediction, but do not provide the monotonic property that is expected. We propose to integrate a multi-dimensional mapping function using deep lattice networks (DLNs) into ViSQOL. DLNs also provide some insight into model interpretation and are robust to overfitting, leading to better out-of-sample performance. With the DLN, ViSQOL improved the speech mapping from the previous exponential mapping's .58 MSE to .24 MSE on a mixture of datasets, outperforming the 1-D fitted functions, SVR, as well as PESQ and POLQA. Additionally, we show that the DLN can be used to learn a quantile function that is well calibrated and a useful measure of uncertainty. With this quantile function, the model is able to provide useful quantile intervals for predictions instead of point intervals.

• Speech Processing

• Machine Intelligence