Background: In a single-ended speech quality evaluation, the measurement of Mean
Opinion Score (MOS) is done objectively without the use of clean speech as a reference. In this
work, multiple time-instances or multi-instances features using Multi-Resolution Auditory Model
(MRAM) along with other relevant features such as Mel-Frequency Cepstral Coefficients (MFCC),
and Line Spectral Frequencies (LSF) features are used for single-ended speech quality measurement.
The Voice Activity Detection (VAD) algorithm separates the presence of speech-regions from silence
in a speech signal.
Methods: The multi-instances features are computed using MRAM, MFCC and LSF for different
combinations of speech-regions to capture degradations due to multiple time-localized effects or the
attacks of short-time transient distortions such as impulsive noise and their distinctions from plosive
sounds in speech. These multi-instances features are used for Gaussian Mixture Model (GMM)
MAPPING to compute the objective MOS values, corresponding to all multi-instances features.
Results: The overall objective MOS estimation of the speech signal is calculated by averaging all the
values of objective MOS corresponding to the different multi-instances features of a speech signal.
The results in terms of Pearson’s Correlation Coefficients (PCC) and root mean square error
(RMSE) between the subjective MOS and the estimated overall objective MOS of speech signals are
computed and compared with International Telecommunication Union-telephony (ITU-T) Recommendation
P.563 and recently published works on similar types of databases.
Conclusion: The improved values of PCC and RMSE between the subjective and the estimated
overall objective MOS show the efficacy of the approach.