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This paper presents an efficient approach to spoken term detection (STD) from unstructured audio recordings using word lattices generated off-line from an automatic speech recognition (ASR) system. The approach facilitates open vocabulary STD and focuses specifically on reducing the difference between detection performance obtained for within-vocabulary (IV) and out-of-vocabulary (OOV) search terms...
This paper investigates indexing strategies for open vocabulary spoken term detection (STD) in a lecture speech domain. STD is performed from word lattices generated offline using an automatic speech recognition (ASR) system configured from a meetings task domain. Indexing of lattice paths is performed to avoid exhaustive search of audio segments which can be impractical for extremely large media...
We present a lattice-based STD method for German broadcast news data and compare it to a previously proposed fuzzy search. Due to the important out-of-vocabulary (OOV) problem in German, we evaluate suitable subword indexing units for lattice retrieval. Hybrid lattice retrieval of words and subwords is investigated because of the robust nature of words as an indexing unit. We show that by using efficient...
While spoken term detection (STD) systems based on word indices provide good accuracy, there are several practical applications where it is infeasible or too costly to employ an LVCSR engine. An STD system is presented, which is designed to incorporate a fast phonetic decoding front-end and be robust to decoding errors whilst still allowing for rapid search speeds. This goal is achieved through monophone...
The spoken term detection (STD) task aims to return relevant segments from a spoken archive that contain the query terms whether or not they are in the system vocabulary. This paper focuses on pronunciation modeling for out-of-vocabulary (OOV) terms which frequently occur in STD queries. The STD system described in this paper indexes word-level and sub-word level lattices or confusion networks produced...
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