Error-correction of binary masks using hidden Markov models

Binary masking is a simple and efficient method for source separation, and a high increase in intelligibility can be obtained by applying the target binary mask to noisy speech. The target binary mask can only be calculated under ideal conditions and will contain errors when estimated in real-life applications. This paper proposes a method for correcting these errors. The error-correction is based on a hidden Markov model and uses the Viterbi algorithm to calculate the most probable error-free target binary mask from a target binary mask containing errors. The results demonstrate that it is possible to correct errors in the target binary mask and reduce the noise energy. However, speech energy is also reduced by the error-correction, but the impact on speech intelligibility and speech quality are not established or evaluated in the present study.