  Automated speech intelligibility assessment is pivotal for hearing aid (HA)
development. In this paper, we present three novel methods to improve
intelligibility prediction accuracy and introduce MBI-Net+, an enhanced version
of MBI-Net, the top-performing system in the 1st Clarity Prediction Challenge.
MBI-Net+ leverages Whisper's embeddings to create cross-domain acoustic
features and includes metadata from speech signals by using a classifier that
distinguishes different enhancement methods. Furthermore, MBI-Net+ integrates
the hearing-aid speech perception index (HASPI) as a supplementary metric into
the objective function to further boost prediction performance. Experimental
results demonstrate that MBI-Net+ surpasses several intrusive baseline systems
and MBI-Net on the Clarity Prediction Challenge 2023 dataset, validating the
effectiveness of incorporating Whisper embeddings, speech metadata, and related
complementary metrics to improve prediction performance for HA.
