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The real-world capabilities of objective speech quality measures are limitedsince current measures (1) are developed from simulated data that does notadequately model real environments; or they (2) predict objective scores thatare not always strongly correlated with subjective ratings. Additionally, alarge dataset of real-world signals with listener quality ratings does notcurrently exist, which would help facilitate real-world assessment. In thispaper, we collect and predict the perceptual quality of real-world speechsignals that are evaluated by human listeners. We first collect a large qualityrating dataset by conducting crowdsourced listening studies on two real-worldcorpora. We further develop a novel approach that predicts human qualityratings using a pyramid bidirectional long short term memory (pBLSTM) networkwith an attention mechanism. The results show that the proposed model achievesstatistically lower estimation errors than prior assessment approaches, wherethe predicted scores strongly correlate with human judgments.

A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals