Recognize Foreign Low-Frequency Words With Similar Pairs

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 underestimated 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.