  Speech enhancement has seen great improvement in recent years using
end-to-end neural networks. However, most models are agnostic to the spoken
phonetic content. Recently, several studies suggested phonetic-aware speech
enhancement, mostly using perceptual supervision. Yet, injecting phonetic
features during model optimization can take additional forms (e.g., model
conditioning). In this paper, we conduct a systematic comparison between
different methods of incorporating phonetic information in a speech enhancement
model. By conducting a series of controlled experiments, we observe the
influence of different phonetic content models as well as various
feature-injection techniques on enhancement performance, considering both
causal and non-causal models. Specifically, we evaluate three settings for
injecting phonetic information, namely: i) feature conditioning; ii) perceptual
supervision; and iii) regularization. Phonetic features are obtained using an
intermediate layer of either a supervised pre-trained Automatic Speech
Recognition (ASR) model or by using a pre-trained Self-Supervised Learning
(SSL) model. We further observe the effect of choosing different embedding
layers on performance, considering both manual and learned configurations.
Results suggest that using a SSL model as phonetic features outperforms the ASR
one in most cases. Interestingly, the conditioning setting performs best among
the evaluated configurations.
