Acoustic TextTiling for story segmentation of spoken documents

We propose an acoustic TextTiling method based on segmental dynamic time warping for automatic story segmentation of spoken documents. Different from most of the existing methods using LVCSR transcripts, this method detects story boundaries directly from audio streams. In analogy to the cosine-based lexical similarity between two text blocks in a transcript, we define the acoustic similarity measure between two pseudo-sentences in an audio stream. Experiments on TDT2 Mandarin corpus show that acoustic TextTiling can achieve comparable performance to lexical TextTiling based on LVCSR transcripts. Moreover, we use MFCCs and Gaussian posteriorgrams as the acoustic representations in our experiments. Our experiments show that Gaussian posteriorgrams are more robust to perform segmentation for the stories each with multiple speakers.