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Статті в журналах з теми "Term detection in multilingual speech"
Karayiğit, Habibe, Ali Akdagli, and Çiğdem İnan Aci. "Homophobic and Hate Speech Detection Using Multilingual-BERT Model on Turkish Social Media." Information Technology and Control 51, no. 2 (June 23, 2022): 356–75. http://dx.doi.org/10.5755/j01.itc.51.2.29988.
Повний текст джерелаDeekshitha, G., and Leena Mary. "Multilingual spoken term detection: a review." International Journal of Speech Technology 23, no. 3 (July 22, 2020): 653–67. http://dx.doi.org/10.1007/s10772-020-09732-9.
Повний текст джерелаCorazza, Michele, Stefano Menini, Elena Cabrio, Sara Tonelli, and Serena Villata. "A Multilingual Evaluation for Online Hate Speech Detection." ACM Transactions on Internet Technology 20, no. 2 (May 25, 2020): 1–22. http://dx.doi.org/10.1145/3377323.
Повний текст джерелаElouali, Aya, Zakaria Elberrichi, and Nadia Elouali. "Hate Speech Detection on Multilingual Twitter Using Convolutional Neural Networks." Revue d'Intelligence Artificielle 34, no. 1 (February 29, 2020): 81–88. http://dx.doi.org/10.18280/ria.340111.
Повний текст джерелаGhosh, Hiranmay, Sunil Kumar Kopparapu, Tanushyam Chattopadhyay, Ashish Khare, Sujal Subhash Wattamwar, Amarendra Gorai, and Meghna Pandharipande. "Multimodal Indexing of Multilingual News Video." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–18. http://dx.doi.org/10.1155/2010/486487.
Повний текст джерелаWijonarko, Panji, and Amalia Zahra. "Spoken language identification on 4 Indonesian local languages using deep learning." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3288–93. http://dx.doi.org/10.11591/eei.v11i6.4166.
Повний текст джерелаMa, Yiping, and Wei Wang. "MSFL: Explainable Multitask-Based Shared Feature Learning for Multilingual Speech Emotion Recognition." Applied Sciences 12, no. 24 (December 13, 2022): 12805. http://dx.doi.org/10.3390/app122412805.
Повний текст джерелаVashistha, Neeraj, and Arkaitz Zubiaga. "Online Multilingual Hate Speech Detection: Experimenting with Hindi and English Social Media." Information 12, no. 1 (December 22, 2020): 5. http://dx.doi.org/10.3390/info12010005.
Повний текст джерелаPopli, Abhimanyu, and Arun Kumar. "Multilingual query-by-example spoken term detection in Indian languages." International Journal of Speech Technology 22, no. 1 (January 10, 2019): 131–41. http://dx.doi.org/10.1007/s10772-018-09585-3.
Повний текст джерелаThanvanthri, Srinedhi, and Shivani Ramakrishnan. "Performance of Text Classification Methods in Detection of Hate Speech in Media." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 354–58. http://dx.doi.org/10.22214/ijraset.2022.40567.
Повний текст джерелаДисертації з теми "Term detection in multilingual speech"
Fancellu, Federico. "Computational models for multilingual negation scope detection." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33038.
Повний текст джерелаCesbron, Fred́eŕique Chantal. "Pitch detection using the short-term phase spectrum." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/15503.
Повний текст джерелаWallace, Roy Geoffrey. "Fast and accurate phonetic spoken term detection." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/39610/1/Roy_Wallace_Thesis.pdf.
Повний текст джерелаZhang, Yaodong Ph D. Massachusetts Institute of Technology. "Unsupervised speech processing with applications to query-by-example spoken term detection." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/79217.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 163-173).
This thesis is motivated by the challenge of searching and extracting useful information from speech data in a completely unsupervised setting. In many real world speech processing problems, obtaining annotated data is not cost and time effective. We therefore ask how much can we learn from speech data without any transcription. To address this question, in this thesis, we chose the query-by-example spoken term detection as a specific scenario to demonstrate that this task can be done in the unsupervised setting without any annotations. To build the unsupervised spoken term detection framework, we contributed three main techniques to form a complete working flow. First, we present two posteriorgram-based speech representations which enable speaker-independent, and noisy spoken term matching. The feasibility and effectiveness of both posteriorgram features are demonstrated through a set of spoken term detection experiments on different datasets. Second, we show two lower-bounding based methods for Dynamic Time Warping (DTW) based pattern matching algorithms. Both algorithms greatly outperform the conventional DTW in a single-threaded computing environment. Third, we describe the parallel implementation of the lower-bounded DTW search algorithm. Experimental results indicate that the total running time of the entire spoken detection system grows linearly with corpus size. We also present the training of large Deep Belief Networks (DBNs) on Graphical Processing Units (GPUs). The phonetic classification experiment on the TIMIT corpus showed a speed-up of 36x for pre-training and 45x for back-propagation for a two-layer DBN trained on the GPU platform compared to the CPU platform.
by Yaodong Zhang.
Ph.D.
Kalantari, Shahram. "Improving spoken term detection using complementary information." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/90074/1/Shahram_Kalantari_Thesis.pdf.
Повний текст джерелаLau, Suk-han. "The effect of type and level of noise on long-term average speech spectrum (LTASS) /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B17896253.
Повний текст джерела劉淑 and Suk-han Lau. "The effect of type and level of noise on long-term average speech spectrum (LTASS)." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31251031.
Повний текст джерелаAbbs, Brandon Robert. "The temporal dynamics of auditory memory for static and dynamic sounds." Diss., University of Iowa, 2008. http://ir.uiowa.edu/etd/4.
Повний текст джерелаRouvier, Mickaël. "Structuration de contenus audio-visuel pour le résumé automatique." Thesis, Avignon, 2011. http://www.theses.fr/2011AVIG0192/document.
Повний текст джерелаThese last years, with the advent of sites such as Youtube, Dailymotion or Blip TV, the number of videos available on the Internet has increased considerably. The size and their lack of structure of these collections limit access to the contents. Sum- marization is one way to produce snippets that extract the essential content and present it as concisely as possible.In this work, we focus on extraction methods for video summary, based on au- dio analysis. We treat various scientific problems related to this objective : content extraction, document structuring, definition and estimation of objective function and algorithm extraction.On each of these aspects, we make concrete proposals that are evaluated.On content extraction, we present a fast spoken-term detection. The main no- velty of this approach is that it relies on the construction of a detector based on search terms. We show that this strategy of self-organization of the detector im- proves system robustness, which significantly exceeds the classical approach based on automatic speech recogntion.We then present an acoustic filtering method for automatic speech recognition based on Gaussian mixture models and factor analysis as it was used recently in speaker identification. The originality of our contribution is the use of decomposi- tion by factor analysis for estimating supervised filters in the cepstral domain.We then discuss the issues of structuring video collections. We show that the use of different levels of representation and different sources of information in or- der to characterize the editorial style of a video is principaly based on audio analy- sis, whereas most previous works suggested that the bulk of information on gender was contained in the image. Another contribution concerns the type of discourse identification ; we propose low-level models for detecting spontaneous speech that significantly improve the state of the art for this kind of approaches.The third focus of this work concerns the summary itself. As part of video summarization, we first try, to define what a synthetic view is. Is that what cha- racterizes the whole document, or what a user would remember (by example an emotional or funny moment) ? This issue is discussed and we make some concrete proposals for the definition of objective functions corresponding to three different criteria : salience, expressiveness and significance. We then propose an algorithm for finding the sum of the maximum interest that derives from the one introduced in previous works, based on integer linear programming
Popli, Abhimanyu. "Framework for query-by-example and text based spoken term detection in multilingual and mixlingual speech." Thesis, 2018. http://localhost:8080/iit/handle/2074/7640.
Повний текст джерелаЧастини книг з теми "Term detection in multilingual speech"
Mary, Leena, and Deekshitha G. "Spoken Term Detection Techniques." In SpringerBriefs in Speech Technology, 61–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97761-4_5.
Повний текст джерелаŠvec, Jan, Luboš Šmídl, and Josef V. Psutka. "An Analysis of the RNN-Based Spoken Term Detection Training." In Speech and Computer, 119–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66429-3_11.
Повний текст джерелаItoh, Yoshiaki, Hiroyuki Saito, Kazuyo Tanaka, and Shi-wook Lee. "Pseudo Real-Time Spoken Term Detection Using Pre-retrieval Results." In Speech and Computer, 264–70. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01931-4_35.
Повний текст джерелаFiguerola, Carlos G., Angel F. Zazo, José L. Alonso Berrocal, and Emilio Rodríguez Vázquez de Aldana. "Interactive and Bilingual Question Answering Using Term Suggestion and Passage Retrieval." In Multilingual Information Access for Text, Speech and Images, 363–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11519645_37.
Повний текст джерелаLee, Kyung-Soon, and Kyo Kageura. "Multilingual Story Link Detection Based on Event Term Weighting on Times and Multilingual Spaces." In Digital Libraries: International Collaboration and Cross-Fertilization, 398–407. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30544-6_43.
Повний текст джерелаVavruška, Jan, Jan Švec, and Pavel Ircing. "Phonetic Spoken Term Detection in Large Audio Archive Using the WFST Framework." In Text, Speech, and Dialogue, 402–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40585-3_51.
Повний текст джерелаDibya, Ranjan Das Adhikary, Jitesh Pradhan, Abhinav Kumar, and Brijendra Pratap Singh. "A Multilingual Review of Hate Speech Detection in Social Media Content." In Cybercrime in Social Media, 85–106. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003304180-5.
Повний текст джерелаLahoti, Anshul, Gurugubelli Krishna, Juan Rafel Orozco Arroyave, and Anil Kumar Vuppala. "Long-Term Average Spectral Feature-Based Parkinson’s Disease Detection from Speech." In Lecture Notes in Electrical Engineering, 603–12. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0840-8_46.
Повний текст джерелаVelankar, Abhishek, Hrushikesh Patil, and Raviraj Joshi. "Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi." In Artificial Neural Networks in Pattern Recognition, 121–28. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20650-4_10.
Повний текст джерелаBhagat, Dhritesh, Aritra Ray, Adarsh Sarda, Nilanjana Dutta Roy, Mufti Mahmud, and Debashis De. "Improving Mental Health Through Multimodal Emotion Detection from Speech and Text Data Using Long-Short Term Memory." In Lecture Notes in Networks and Systems, 13–23. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5191-6_2.
Повний текст джерелаТези доповідей конференцій з теми "Term detection in multilingual speech"
Anguera, Xavier, Luis Javier Rodriguez-Fuentes, Igor Szőke, Andi Buzo, Florian Metze, and Mikel Penagarikano. "Query-by-example spoken term detection on multilingual unconstrained speech." In Interspeech 2014. ISCA: ISCA, 2014. http://dx.doi.org/10.21437/interspeech.2014-522.
Повний текст джерелаKnill, K. M., M. J. F. Gales, S. P. Rath, P. C. Woodland, C. Zhang, and S. X. Zhang. "Investigation of multilingual deep neural networks for spoken term detection." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707719.
Повний текст джерелаRam, Dhananjay, Lesly Miculicich, and Herve Bourlard. "Multilingual Bottleneck Features for Query by Example Spoken Term Detection." In 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2019. http://dx.doi.org/10.1109/asru46091.2019.9003752.
Повний текст джерелаBuzo, Andi, Horia Cucu, Mihai Safta, and Corneliu Burileanu. "Multilingual query by example spoken term detection for under-resourced languages." In 2013 7th Conference on Speech Technology and Human - Computer Dialogue (SpeD 2013). IEEE, 2013. http://dx.doi.org/10.1109/sped.2013.6682655.
Повний текст джерелаMullick, Ankan. "Exploring Multilingual Intent Dynamics and Applications." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/818.
Повний текст джерелаKapil, Prashant, and Asif Ekbal. "A Transformer based Multi-Task Learning Approach Leveraging Translated and Transliterated Data to Hate Speech Detection in Hindi." In 3rd International Conference on Data Science and Machine Learning (DSML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121516.
Повний текст джерелаMotlicek, Petr, Fabio Valente, and Philip N. Garner. "English spoken term detection in multilingual recordings." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-86.
Повний текст джерелаArango Monnar, Ayme, Jorge Perez, Barbara Poblete, Magdalena Saldaña, and Valentina Proust. "Resources for Multilingual Hate Speech Detection." In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.woah-1.12.
Повний текст джерелаRöttger, Paul, Haitham Seelawi, Debora Nozza, Zeerak Talat, and Bertie Vidgen. "Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models." In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.woah-1.15.
Повний текст джерелаSarfjoo, Seyyed Saeed, Srikanth Madikeri, and Petr Motlicek. "Speech Activity Detection Based on Multilingual Speech Recognition System." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1058.
Повний текст джерела