Unseen but not Unknown: Using Dataset Concealment to Robustly Evaluate Speech Quality Estimation Models

We introduce Dataset Concealment (DSC), a rigorous new procedure for evaluating and interpreting objective speech quality estimation models. DSC quantifies and decomposes the performance gap between research results and real-world application requirements, while offering context and additional insights into model behavior and dataset characteristics. We also show the benefits of addressing the corpus effect by using the dataset Aligner from AlignNet when training models with multiple datasets. We demonstrate DSC and the improvements from the Aligner using nine training datasets and nine unseen datasets with three well-studied models: MOSNet, NISQA, and a Wav2Vec2.0-based model. DSC provides interpretable views of the generalization capabilities and limitations of models, while allowing all available data to be used at training. An additional result is that adding the 1000 parameter dataset Aligner to the 94 million parameter Wav2Vec model during training does significantly improve the resulting model's ability to estimate speech quality for unseen data.

Why some audio signal short-time Fourier transform coefficients have nonuniform phase distributions

The short-time Fourier transform (STFT) represents a window of audio samples as a set of complex coefficients. These are advantageously viewed as magnitudes and phases and the overall distribution of phases is very often assumed to be uniform. We show that when audio signal STFT phase distributions are analyzed per-frequency or per-magnitude range, they can be far from uniform. That is, the uniform phase distribution assumption obscures significant important details. We explain the significance of the nonuniform phase distributions and how they might be exploited, derive their source, and explain why the choice of the STFT window shape influences the nonuniformity of the resulting phase distributions.

WEnets: A Convolutional Framework for Evaluating Audio Waveforms

We describe a new convolutional framework for waveform evaluation, WEnets, and build a Narrowband Audio Waveform Evaluation Network, or NAWEnet, using this framework. NAWEnet is single-ended (or no-reference) and was trained three separate times in order to emulate PESQ, POLQA, or STOI with testing correlations 0.95, 0.92, and 0.95, respectively when training on only 50% of available data and testing on 40%. Stacks of 1-D convolutional layers and non-linear downsampling learn which features are important for quality or intelligibility estimation. This straightforward architecture simplifies the interpretation of its inner workings and paves the way for future investigations into higher sample rates and accurate no-reference subjective speech quality predictions.