دانلود ترجمه مقاله شناسایی عبارات گفتاری در عبارت خارج از واژگان OOV

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Abstract—The goal of Spoken Term Detection (STD) technology is to allow open vocabulary search over large collections of speech content. In this paper, we address cases where search term(s) of interest (queries) are acoustic examples. This is provided either by identifying a region of interest in a speech stream or by speaking the query term. Queries often relate to named-entities and foreign words, which typically have poor coverage in the vocabulary of Large Vocabulary Continuous Speech Recognition (LVCSR) systems. Throughout this paper, we focus on query-by-example search for such out-of-vocabulary (OOV) query terms. We build upon a finite state transducer (FST) based search and indexing system [1] to address the query by example search for OOV terms by representing both the query and the index as phonetic lattices from the output of an LVCSR system. We provide results comparing different representations and generation mechanisms for both queries and indexes built with word and combined word and subword units [2]. We also present a two-pass method which uses query-by-example search using the best hit identified in an initial pass to augment the STD search results.

 The results demonstrate that query by-example search can yield a significantly better performance, measured using Actual Term-Weighted Value (ATWV), of 0.479 when compared to a baseline ATWV of 0.325 that uses reference pronunciations for OOVs. Further improvements can be obtained with the proposed two pass approach and filtering using the expected unigram counts from the LVCSR system’s lexicon.
I. INTRODUCTION
The fast-growing availability of recorded speech calls for efficient and scalable solutions to index and search this data. Spoken Term Detection (STD) is a key technology aimed at open-vocabulary search over large collections of spoken documents. A common approach to STD is to employ a large vocabulary continuous speech recognition (LVCSR) system to obtain word lattices and extend classical Information Retrieval techniques to word lattices. Such approaches have been shown to be very accurate for well-resourced tasks [3], [1].