Effectiveness of ideal ratio mask for non-intrusive quality assessment of noise suppressed speech

The Ideal Ratio Mask (IRM) has proven to be very effective tool in many applications such as speech segregation, speech enhancement for hearing aid design and noise robust speech recognition tasks. The IRM provides information regarding the amount of signal power at each Time-Frequency (T-F) unit in a given signal-plus-noise mixture. In this paper, we propose to use the IRM for non-intrusive quality assessment of noise suppressed speech. Since the quality of noise suppressed speech is dependent on the residual noise present in speech, IRM can be extremely useful for its quality assessment. The quality assessment problem is posed as a regression problem and the mapping between statistics of acoustic features, namely, Mel Filterbank Energies (FBEs) plus IRM features and the subjective score of the corresponding utterances was found using single-layer Artificial Neural Network (ANN). The results of our experiments suggest that by using the mean of FBEs and IRM features as the input, the quality prediction accuracy was significantly increased.