A multi-pass linear fold algorithm for sentence boundary detection using prosodic cues

We propose a multi-pass linear fold algorithm for sentence boundary detection in spontaneous speech. It uses only prosodic cues and does not rely on segmentation information from a speech recognition decoder. We focus on features based on pitch breaks and pitch durations, study their local and global structural properties and find their relationship with sentence boundaries. In the first step, the algorithm, which requires no training, automatically finds a set of candidate pitch breaks by simple curve fitting. In the next step, by exploiting statistical properties of sentence boundaries and disfluency, the algorithm finds the sentence boundaries within these candidate pitch breaks. With this simple method without any explicit segmentation information from an ASR, a 25% error rate was achieved on a randomly selected portion of the switchboard corpus. The result from this method is comparable with those that include word segmentation information and can be used in conjunction to improve the overall performance and confidence.