Detecting Repetitions in Spoken Dialogue Systems Using Phonetic Distances

This paper addresses the problem of automatic detection of re-peated turns in Spoken Dialogue Systems. Repetitions can be a symptom of problematic communication between users and systems. Such repetitions are often due to speech recognition errors, which in turn makes it hard to use speech recognition to detect repetitions. We present an approach to detect rep-etition using the phonetic distance to find the best alignment between turns in the same dialogue. The alignment score ob-tained is combined with different features to improve repeti-tion detection. To evaluate the method proposed we compare several alignment techniques from edit distance to DTW-based distance, previously used in Spoken-Term detection tasks. We also compare two different methods to compute the phonetic distance: the first one using the phoneme sequence, and the second one using the distance between the phone posterior vec-tors. Two different datasets were used in this evaluation: a bus-schedule information system (in English) and a call routing system (in Swedish). The results show that approaches using phoneme distances over-perform approaches using Levenshtein distances between ASR outputs for repetition detection. Index Terms: spoken dialogue systems, repetition detection, phonetic distanc