Kneser-Ney smoothng on expected counts was proposed recently. In this paper we revisit this technique and suggest a number of optimizations and extensions. We then analyse its performance in several practical speech recognition scenarios that depend on fractional sample counts, such as training on uncertain data, language model adaptation and Word-Phrase-Entity models. We show that the proposed approach to smoothing outperforms known alternatives by a significant margin.