CMU robust vocabulary-independent speech recognition system

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Abstract
Efforts to improve the performance of CMU's robust vocabulary-independent (VI) speech recognition systems on the DARPA speaker-independent resource management task are discussed. The improvements are evaluated on 320 sentences randomly selected from the DARPA June 88, February 89, and October 89 test sets. The first improvement involves more detailed acoustic modeling. The authors incorporated more dynamic features computed from the LPC cepstra and reduced error by 15% over the baseline system. The second improvement comes from a larger database. With more training data, the third improvement comes from a more detailed subword modeling. The authors incorporated the word boundary context into their VI subword modeling and it resulted in a 30% error reduction. Decision-tree allophone clustering was used to find more suitable models for the subword units not covered in the training set and further reduced error by 17%.

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