  Low-frequency words place a major challenge for automatic speech recognition
(ASR). The probabilities of these words, which are often important name
entities, are generally under-estimated by the language model (LM) due to their
limited occurrences in the training data. Recently, we proposed a word-pair
approach to deal with the problem, which borrows information of frequent words
to enhance the probabilities of low-frequency words. This paper presents an
extension to the word-pair method by involving multiple `predicting words' to
produce better estimation for low-frequency words. We also employ this approach
to deal with out-of-language words in the task of multi-lingual speech
recognition.
