Audio thumbnails for spoken content without transcription based on a maximum motif coverage criterion

The paper presents a system to create audio thumbnails of spoken content, i.e., short audio summaries representative of the entire content, without resorting to a lexical representation. As an alternative to searching for relevant words and phrases in a transcript, unsupervised motif discovery is used to find short, word-like, repeating fragments at the signal level without acoustic models. The output of the word discovery algorithm is exploited via a maximum motif coverage criterion to generate a thumbnail in an extractive manner. A limited number of relevant segments are chosen within the data so as to include the maximum number of motifs while remaining short enough and intelligible. Evaluation is performed on broadcast news reports with a panel of human listeners judging the quality of the thumbnails. Results indicate that motif-based thumbnails stand between random thumbnails and ASR-based keywords, however still far behind thumbnails and keywords humanly authored.