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Without the need of a clean reference, non-intrusive speech assessmentmethods have caught great attention for objective evaluations. Recently, deepneural network (DNN) models have been applied to build non-intrusive speechassessment approaches and confirmed to provide promising performance. However,most DNN-based approaches are designed for normal-hearing listeners withoutconsidering hearing-loss factors. In this study, we propose a DNN-based hearingaid speech assessment network (HASA-Net), formed by a bidirectional longshort-term memory (BLSTM) model, to predict speech quality and intelligibilityscores simultaneously according to input speech signals and specifiedhearing-loss patterns. To the best of our knowledge, HASA-Net is the first workto incorporate quality and intelligibility assessments utilizing a unifiedDNN-based non-intrusive model for hearing aids. Experimental results show thatthe predicted speech quality and intelligibility scores of HASA-Net are highlycorrelated to two well-known intrusive hearing-aid evaluation metrics, hearingaid speech quality index (HASQI) and hearing aid speech perception index(HASPI), respectively.

HASA-net: A non-intrusive hearing-aid speech assessment network