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Developers of text-to-speech synthesizers (TTS) often make use of humanraters to assess the quality of synthesized speech. We demonstrate that we canmodel human raters' mean opinion scores (MOS) of synthesized speech using adeep recurrent neural network whose inputs consist solely of a raw waveform.Our best models provide utterance-level estimates of MOS only moderatelyinferior to sampled human ratings, as shown by Pearson and Spearmancorrelations. When multiple utterances are scored and averaged, a scenariocommon in synthesizer quality assessment, AutoMOS achieves correlationsapproaching those of human raters. The AutoMOS model has a number ofapplications, such as the ability to explore the parameter space of a speechsynthesizer without requiring a human-in-the-loop.

AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech