  This research introduces an enhanced version of the multi-objective speech
assessment model--MOSA-Net+, by leveraging the acoustic features from Whisper,
a large-scaled weakly supervised model. We first investigate the effectiveness
of Whisper in deploying a more robust speech assessment model. After that, we
explore combining representations from Whisper and SSL models. The experimental
results reveal that Whisper's embedding features can contribute to more
accurate prediction performance. Moreover, combining the embedding features
from Whisper and SSL models only leads to marginal improvement. As compared to
intrusive methods, MOSA-Net, and other SSL-based speech assessment models,
MOSA-Net+ yields notable improvements in estimating subjective quality and
intelligibility scores across all evaluation metrics in Taiwan Mandarin Hearing
In Noise test - Quality & Intelligibility (TMHINT-QI) dataset. To further
validate its robustness, MOSA-Net+ was tested in the noisy-and-enhanced track
of the VoiceMOS Challenge 2023, where it obtained the top-ranked performance
among nine systems.
