Morpheme concatenation approach in language modeling for large-vocabulary Uyghur speech recognition

For large-vocabulary continuous speech recognition (LVCSR) of highly-inflected languages, selection of an appropriate recognition unit is the first important step. The morpheme-based approach is often adopted because of its high coverage and linguistic properties. But morpheme units are short, often consisting of one or two phonemes, thus they are more likely to be confused in ASR than word units. Generally, word units provide better linguistic constraint, but increases the vocabulary size explosively, causing OOV (out-of-vocabulary) and data sparseness problems in language modeling. In this research, we investigate approaches of selecting word entries by concatenating morpheme sequences, which would reduce word error rate (WER). Specifically, we compare the ASR results of the word-based model and those of the morpheme-based model, and extract typical patterns which would reduce the WER. This method has been successfully applied to an Uyghur LVCSR system, resulting in a significant reduction of WER without a drastic increase of the vocabulary size.