Unsupervised acoustic sub-word unit detection for query-by-example spoken term detection

In this paper we present a method for automatically generating acoustic sub-word units that can substitute conventional phone models in a query-by-example spoken term detection system. We generate the sub-word units with a modified version of our speaker diarization system. Given a speech recording, the original diarization system generates a set of speaker models in an unsupervised manner without the need for training or development data. Modifying the diarization system to process the speech of a single speaker and decreasing the minimum segment duration constraint allows us to detect speaker-dependent sub-word units. For the task of query-by-example spoken term detection, we show that the pro posed system performs well on both broadcast and non-broadcast recordings, unlike a conventional phone-based system trained solely on broadcast data. A mean average precision of 0.28 and 0.38 was obtained for experiments on broadcast news and on a set of war veteran interviews, respectively.