Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

HMM word graph based keyword spotting in handwritten document images

[EN] Line-level keyword spotting (KWS) is presented on the basis of frame-level word posterior probabilities. These posteriors are obtained using word graphs derived from the recogni- tion process of a full-fledged handwritten text recognizer based on hidden Markov models and N-gram language models. This approach has several advantages. First, since it uses a holistic, segmentation-free technology, it does not require any kind of word or charac- ter segmentation. Second, the use of language models allows the context of each spotted word to be taken into account, thereby considerably increasing KWS accuracy. And third, the proposed KWS scores are based on true posterior probabilities, taking into account all (or most) possible word segmentations of the input image. These scores are properly bounded and normalized. This mathematically clean formulation lends itself to smooth, threshold-based keyword queries which, in turn, permit comfortable trade-offs between search precision and recall. Experiments are carried out on several historic collections of handwritten text images, as well as a well-known data set of modern English handwrit- ten text. According to the empirical results, the proposed approach achieves KWS results comparable to those obtained with the recently-introduced "BLSTM neural networks KWS" approach and clearly outperform the popular, state-of-the-art "Filler HMM" KWS method. Overall, the results clearly support all the above-claimed advantages of the proposed ap- proach.This work has been partially supported by the Generalitat Valenciana under the Prometeo/2009/014 project grant ALMA-MATER, and through the EU projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon 2020 programme, grant Ref. 674943).Toselli, AH.; Vidal, E.; Romero, V.; Frinken, V. (2016). HMM word graph based keyword spotting in handwritten document images. Information Sciences. 370:497-518. https://doi.org/10.1016/j.ins.2016.07.063S49751837

Querying out-of-vocabulary words in lexicon-based keyword spotting

The final publication is available at Springer via http://dx.doi.org/10.1007/s00521-016-2197-8[EN] Lexicon-based handwritten text keyword spotting (KWS) has proven to be a faster and more accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS relies on a predefined vocabulary, fixed in the training phase, it does not support queries involving out-of-vocabulary (OOV) keywords. In this paper, we outline previous work aimed at solving this problem and present a new approach based on smoothing the (null) scores of OOV keywords by means of the information provided by ``similar'' in-vocabulary words. Good results achieved using this approach are compared with previously published alternatives on different data sets.This work was partially supported by the Spanish MEC under FPU Grant FPU13/06281, by the Generalitat Valenciana under the Prometeo/2009/014 Project Grant ALMA-MATER, and through the EU Projects: HIMANIS (JPICH programme, Spanish grant Ref. PCIN-2015-068) and READ (Horizon-2020 programme, grant Ref. 674943).Puigcerver, J.; Toselli, AH.; Vidal, E. (2016). Querying out-of-vocabulary words in lexicon-based keyword spotting. Neural Computing and Applications. 1-10. https://doi.org/10.1007/s00521-016-2197-8S110Almazan J, Gordo A, Fornes A, Valveny E (2013) Handwritten word spotting with corrected attributes. In: 2013 IEEE international conference on computer vision (ICCV), pp 1017–1024. doi: 10.1109/ICCV.2013.130Amengual JC, Vidal E (2000) On the estimation of error-correcting parameters. In: Proceedings 15th international conference on pattern recognition, 2000, vol 2, pp 883–886Fernández D, Lladós J, Fornés A (2011) Handwritten word spotting in old manuscript images using a pseudo-structural descriptor organized in a hash structure. In: Vitri'a J, Sanches JM, Hern'andez M (eds) Pattern recognition and image analysis: Proceedings of 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8–10. 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Keyword spotting for audiovisual archival search in Uralic languages

Publisher Copyright: © 2021 IWCLUL 2021 - 7th International Workshop on Computational Linguistics of Uralic Languages, Proceedings. All rights reserved.In this study we investigate the potential of using Automatic Speech Recognition (ASR) for keyword spotting for four Uralic languages: Finnish, Hungarian, Estonian and Komi. These languages also represent different levels on the high and low resource continuum. Although the accuracy of the ASR systems show there is a long way to go, we show that they still have potential to be useful for downstream tasks such as keyword spotting. By using a simple text search after running ASR, we are already able to achieve an F1 score of between 0.15 and 0.33, a precision of nearly 0.90 for Estonian and Hungarian, and a precision of 0.76 for Komi.Peer reviewe