Automatic no-reference speech quality assessment with convolutional neural networks


In this paper, it is presented a convolutional neural network model to address the automatic speech quality assessment problem. It is a no-reference methodology that applies convolutional layers as feature extractors for visual representation through Mel-Frequency Cepstral Coefficients of the speech signal. Its performance is evaluated through comparison to the methodologies PESQ, ViSQOL and P.563. The experiments were conducted in publicly available databases and in another database that was built to evaluate our model in the context of background noise. The results are analyzed by means of correlation measures and statistical descriptions. Through four experiments, we have concluded that: (1) our model achieved high overall generalization, even when it was trained with a limited quantity of samples; (2) it also characterized speech and background sound even for databases where complex degradation is present; and (3) the proposed model tends to assign high scores to clean speech and low scores to samples with just noise, right as expected.

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The authors would like to thank the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research.

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Correspondence to Carlos A. B. Mello.

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Albuquerque, R.Q., Mello, C.A.B. Automatic no-reference speech quality assessment with convolutional neural networks. Neural Comput & Applic (2021).

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  • Speech quality assessment
  • Convolutional neural networks
  • Mel-frequency cepstral coefficient