We proposed and evaluated a speech intelligibility estimation method that does not require a clean speech reference signal. The propose method uses the features defined in the ITU-T standard P.563, which estimates the overall quality of speech without the reference signal. We selected two sets of features from the P.563 features; the basic 9-feature set, which includes basic features that characterize both speech and background noise, e.g., cepstrum skewness and LPC kurtosis, and the extended 31-feature set with 22 additional features for a more accurate description of the degraded speech and noise, e.g., SNR, average pitch, and spectral clarity among others. Four hundred noise samples were added to speech, and about 70% of these samples were used to train a support vector regression (SVR) model. The trained models were used to estimate the intelligibility of speech degraded by added noise. The proposed method showed a root mean square error (RMSE) value of about 10% and correlation with subjective intelligibility of about 0.93 for speech distorted with known noise type, and RMSE of about 16% and a correlation of about 0.84 for speech distorted with unknown noise type, both with either the 9 or the 31-dimension feature set. These results were higher than the estimation using frequency-weighed SNR calculated in critical frequency bands, which requires the clean reference signal for its calculation. We believe this level of accuracy proves the proposed method to be applicable to real-time speech quality monitoring in the field.

This paper describes non-reference objective quality evaluation for noise-reduced speech. First, a subjective test is conducted in accordance with ITU-T Rec. P.835 to obtain the speech quality, the noise quality, and the overall quality of noise-reduced speech. Based on the results, we then propose an overall quality estimation model. The unique point of the proposed model is that the estimation of the overall quality is done only using the previously estimated speech quality and noise quality, in contrast to conventional models, which utilize the acoustical features extracted. Finally, we propose a non-reference objective quality evaluation method using the proposed model. The results of an experiment with different noise reduction algorithms and noise types confirmed that the proposed method gives more accurate estimates of the overall quality compared with the method described in ITU-T Rec. P.563.