text: 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.
  @cite_7: In practical automatic speech recognition (ASR) systems, it is difficult to recognize words that are with low-frequency in the language model (LM) training data. Ironically, these words tend to be highly important as they are often domain-specific name entities. In order to meet this challenge, we present a novel approach that enhances the weights of these words by borrowing information from some high-frequency words that are similar to the target words. Experimental results demonstrated that our method can significantly improve ASR performance on low-frequency words and does not impact performance on high-frequency words. Additionally, this method can be easily extended to deal with new words that are absent in the LM training data.
summary: The extensions we made in this paper for the work in @cite_7 are two-fold: firstly, the similar-pair algorithm is extended to allow multiple predicting words, which enables multiple information engaged and thus better enhancement; second, the similar-pair approach is employed to deal with OOL words, which demonstrated that similar pairs can be cross-lingual.