New methods for the analysis of repeated utterances

This paper proposes three novel and effective procedures for jointly analyzing repeated utterances. First, we propose repetition-driven system switching, where repetition triggers the use of an independent backup system for decoding. Second, we propose a cache language model for use with the second utterance. Finally, we propose a method with which the acoustics from multiple utterances not necessarily exact repetitions of each other can be combined to into a composite that increases accuracy. The combination of all methods produces a relative increase in sentence accuracy of 65.7% for repeated voice-search queries.