A Method to Detect Self-repair Syllable Strings in Spontaneous Speech using Markov Model

This paper proposes a method to detect self-repair strings included in spontaneous speech by Markov models of syllables. These strings are assumed to be represented with syllable strings obtained correctly by acoustic processing. The method comprises the following two steps: The first step is to determine the provisional bunsetsu boundaries of a non-segmented syllable sentence with self-repair strings. We improved the method which has been proposed to find the provisional bunsetsu boundaries of correct sentences by Markov models, to be applicable to sentences with self-repair. The second step is to detect self-repair strings, which are inserted in the location of bunsetsu boundaries. In this step, we proposed three methods of pattern matching to detect these strings. This method is applied to detect self-repair strings in ATR dialogue corpus. It is confirmed that the method is effective to detect self-repair strings inserted in bunsetsu boundaries.