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The framework of posteriorgram-based template matching has been shown to be successful for query-by-example spoken term detection (STD). This framework employs a tokenizer to convert query examples and test utterances into frame-level posteriorgrams, and applies dynamic time warping to match the query posteriorgrams with test posteriorgrams to locate possible occurrences of the query term. It is not...
This paper describes a study on query-by-example spoken term detection (STD) using the acoustic segment modeling technique. Acoustic segment models (ASMs) are a set of hidden Markov models (HMM) that are obtained in an unsupervised manner without using any transcription information. The training of ASMs follows an iterative procedure, which consists of the steps of initial segmentation, segments labeling,...
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