1. A method of detecting repeated patterns in a dialog system, comprising: receiving an initial utterance from a user in the dialog sequence as a set of test patterns, the initial utterance including a first plurality of words in a first order; receiving a correction utterance from the user, the correction utterance including a second plurality of words in a second order, wherein at least some of the second plurality of words are repeated from the first plurality of words; segmenting the correction utterance into a set of reference patterns by identifying silence regions separating each of the first plurality of words within the initial utterance; extracting feature vectors from the test patterns and the reference patterns; comparing each test pattern with each reference pattern over the entire length of the set of test patterns and the set of reference patterns, using a dynamic time warping process in order to determine boundaries of the reference and test patterns using spectral characteristics of the initial and correction utterances; computing distance scores between each reference pattern and each test pattern; and identifying test patterns with a distance score that is less than a defined distance threshold as a repeated pattern.
1. A method of detecting repeated patterns in a dialog system, comprising: receiving an initial utterance from a user in the dialog sequence as a set of test patterns, the initial utterance including a first plurality of words in a first order; receiving a correction utterance from the user, the correction utterance including a second plurality of words in a second order, wherein at least some of the second plurality of words are repeated from the first plurality of words; segmenting the correction utterance into a set of reference patterns by identifying silence regions separating each of the first plurality of words within the initial utterance; extracting feature vectors from the test patterns and the reference patterns; comparing each test pattern with each reference pattern over the entire length of the set of test patterns and the set of reference patterns, using a dynamic time warping process in order to determine boundaries of the reference and test patterns using spectral characteristics of the initial and correction utterances; computing distance scores between each reference pattern and each test pattern; and identifying test patterns with a distance score that is less than a defined distance threshold as a repeated pattern. 10. The method of claim 1 further comprising: defining reference and test pattern pairs for each possible combination of test and reference patterns; recursively applying a dynamic time warping process on each reference and test pattern pairs; defining a maximum reference pattern length; and identifying matching patterns as reference patterns of a reference and test pattern pair with a distance score that is less than the defined maximum reference pattern length.