A Comparison of Perceptually Motivated Loss Functions for Binary Mask Estimation in Speech Separation

Danny Websdale, Ben Milner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
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This work proposes and compares perceptually motivated loss functions for deep learning based binary mask estimation for speech separation. Previous loss functions have focused on maximising classification accuracy of mask estimation but we now propose loss functions that aim to maximise the hit mi- nus false-alarm (HIT-FA) rate which is known to correlate more closely to speech intelligibility. The baseline loss function is bi- nary cross-entropy (CE), a standard loss function used in binary mask estimation, which maximises classification accuracy. We propose first a loss function that maximises the HIT-FA rate in- stead of classification accuracy. We then propose a second loss function that is a hybrid between CE and HIT-FA, providing a balance between classification accuracy and HIT-FA rate. Eval- uations of the perceptually motivated loss functions with the GRID database show improvements to HIT-FA rate and ESTOI across babble and factory noises. Further tests then explore ap- plication of the perceptually motivated loss functions to a larger vocabulary dataset.
Original languageEnglish
Title of host publicationProceedings of Interspeech 2017
Number of pages5
Publication statusPublished - Aug 2017
EventInterspeech 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017


ConferenceInterspeech 2017
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