  Previous studies have confirmed that by augmenting acoustic features with the
place/manner of articulatory features, the speech enhancement (SE) process can
be guided to consider the broad phonetic properties of the input speech when
performing enhancement to attain performance improvements. In this paper, we
explore the contextual information of articulatory attributes as additional
information to further benefit SE. More specifically, we propose to improve the
SE performance by leveraging losses from an end-to-end automatic speech
recognition (E2E-ASR) model that predicts the sequence of broad phonetic
classes (BPCs). We also developed multi-objective training with ASR and
perceptual losses to train the SE system based on a BPC-based E2E-ASR.
Experimental results from speech denoising, speech dereverberation, and
impaired speech enhancement tasks confirmed that contextual BPC information
improves SE performance. Moreover, the SE model trained with the BPC-based
E2E-ASR outperforms that with the phoneme-based E2E-ASR. The results suggest
that objectives with misclassification of phonemes by the ASR system may lead
to imperfect feedback, and BPC could be a potentially better choice. Finally,
it is noted that combining the most-confusable phonetic targets into the same
BPC when calculating the additional objective can effectively improve the SE
performance.
