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Retrieving spoken content with spoken queries, or query-by- example spokenterm detection (STD), is attractive because it makes possible the matching ofsignals directly on the acoustic level without transcribing them into text.Here, we propose an end-to-end query-by-example STD model based on anattention-based multi-hop network, whose input is a spoken query and an audiosegment containing several utterances; the output states whether the audiosegment includes the query. The model can be trained in either a supervisedscenario using labeled data, or in an unsupervised fashion. In the supervisedscenario, we find that the attention mechanism and multiple hops improveperformance, and that the attention weights indicate the time span of thedetected terms. In the unsupervised setting, the model mimics the behavior ofthe existing query-by-example STD system, yielding performance comparable tothe existing system but with a lower search time complexity.

Query-by-example Spoken Term Detection using Attention-based Multi-hop Networks