Learning N-gram Language Models from Uncertain Data
We present a new algorithm for efficiently training n-gram language models on uncertain data, and illustrate its use for semi-supervised language model adaptation. We compute the probability that an n-gram occurs k times in the sample of uncertain data, and use the resulting histograms to derive a generalized Katz backoff model. We compare semi-supervised adaptation of language models for YouTube video speech recognition in two conditions: when using full lattices with our new algorithm versus just the 1-best output from the baseline speech recognizer. Unlike 1-best methods, the new algorithm provides models that yield solid improvements over the baseline on the full test set, and, further, achieves these gains without hurting performance on any of the set of channels. We show that channels with the most data yielded the largest gains. The algorithm was implemented via a new semiring in the OpenFst library and will be released as part of the OpenGrm ngram library.