Legacy low-bitrate speech codecs such as AMR-NB remain widely used in telephony, where they restrict speech bandwidth and degrade listening quality. Coded speech restoration (CSR) aims to restore the speech bandwidth while improving the overall quality of coded speech, but typically without adapting to different speakers. In this paper, we systematically investigate personalization for restoration of speech coded at low bitrates, using a causal, low-complexity Universal Bandwidth Extension Generative Adversarial Network (UBGAN) model as the basis. We propose two personalization methods based on speaker embeddings, which incur little additional computational cost at inference: (i) conditioning a single CSR model on speaker embeddings, and (ii) cluster-based model selection, where multiple personalized models are trained on clusters of acoustically similar speakers. Experiments are performed on AMR-NB coded speech at 4.75 kbps. Experimental results with disjoint seen- and unseen-speaker test sets show that cluster-based model selection does not consistently outperform the non-personalized UBGAN baseline, whereas embedding conditioning significantly improves ViSQOL and WB-PESQ scores for both seen and unseen speakers.