Literatura académica sobre el tema "Syllable-based ASR"
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Artículos de revistas sobre el tema "Syllable-based ASR"
Galatang, Danny Henry y Suyanto Suyanto. "Syllable-Based Indonesian Automatic Speech Recognition". International Journal on Electrical Engineering and Informatics 12, n.º 4 (31 de diciembre de 2020): 720–28. http://dx.doi.org/10.15676/ijeei.2020.12.4.2.
Texto completoValizada, Alakbar. "DEVELOPMENT OF A REAL-TIME SPEECH RECOGNITION SYSTEM FOR THE AZERBAIJANI LANGUAGE". Problems of Information Society 14, n.º 2 (5 de julio de 2023): 55–60. http://dx.doi.org/10.25045/jpis.v14.i2.07.
Texto completoMahesha, P. y D. S. Vinod. "Gaussian Mixture Model Based Classification of Stuttering Dysfluencies". Journal of Intelligent Systems 25, n.º 3 (1 de julio de 2016): 387–99. http://dx.doi.org/10.1515/jisys-2014-0140.
Texto completoPerrine, Brittany L., Ronald C. Scherer y Jason A. Whitfield. "Signal Interpretation Considerations When Estimating Subglottal Pressure From Oral Air Pressure". Journal of Speech, Language, and Hearing Research 62, n.º 5 (21 de mayo de 2019): 1326–37. http://dx.doi.org/10.1044/2018_jslhr-s-17-0432.
Texto completoRui, Xian Yi, Yi Biao Yu y Ying Jiang. "Connected Mandarin Digit Speech Recognition Using Two-Layer Acoustic Universal Structure". Advanced Materials Research 846-847 (noviembre de 2013): 1380–83. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1380.
Texto completoRong, Panying. "Neuromotor Control of Speech and Speechlike Tasks: Implications From Articulatory Gestures". Perspectives of the ASHA Special Interest Groups 5, n.º 5 (23 de octubre de 2020): 1324–38. http://dx.doi.org/10.1044/2020_persp-20-00070.
Texto completoVan der Burg, Erik y Patrick T. Goodbourn. "Rapid, generalized adaptation to asynchronous audiovisual speech". Proceedings of the Royal Society B: Biological Sciences 282, n.º 1804 (7 de abril de 2015): 20143083. http://dx.doi.org/10.1098/rspb.2014.3083.
Texto completoTruong Tien, Toan. "ASR - VLSP 2021: An Efficient Transformer-based Approach for Vietnamese ASR Task". VNU Journal of Science: Computer Science and Communication Engineering 38, n.º 1 (30 de junio de 2022). http://dx.doi.org/10.25073/2588-1086/vnucsce.325.
Texto completoThanh, Pham Viet, Le Duc Cuong, Dao Dang Huy, Luu Duc Thanh, Nguyen Duc Tan, Dang Trung Duc Anh y Nguyen Thi Thu Trang. "ASR - VLSP 2021: Semi-supervised Ensemble Model for Vietnamese Automatic Speech Recognition". VNU Journal of Science: Computer Science and Communication Engineering 38, n.º 1 (30 de junio de 2022). http://dx.doi.org/10.25073/2588-1086/vnucsce.332.
Texto completoHaley, Katarina L., Adam Jacks, Soomin Kim, Marcia Rodriguez y Lorelei P. Johnson. "Normative Values for Word Syllable Duration With Interpretation in a Large Sample of Stroke Survivors With Aphasia". American Journal of Speech-Language Pathology, 18 de agosto de 2023, 1–13. http://dx.doi.org/10.1044/2023_ajslp-22-00300.
Texto completoTesis sobre el tema "Syllable-based ASR"
DavidSarwono y 林立成. "A Syllable Cluster Based Weighted Kernel Feature Matrix for ASR Substitution Error Correction". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/28550786350424171660.
Texto completo國立成功大學
資訊工程學系碩博士班
100
Abstract A Syllable Cluster Based Weighted Kernel Feature Matrix for ASR Substitution Error Correction David Sarwono * Chung-Hsien Wu** Institute of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C. In recent years Automatic Speech Recognition (ASR) technology has become one of the most growing technologies in engineering science and research. However, the performance of ASR technology is still restricted in adverse environments. Errors in Automatic Speech Recognition outputs lead to low performance for speech applications, therefore correction techniques for these errors will be beneficial to applications relied on ASR outputs. In this study, A Syllable Cluster Based Weighted Kernel Feature Matrix based on Context Dependent Syllable Cluster (CDSC) is proposed for the generation of correction candidates. For candidate selection in the second stage, the n-gram language model is used to determine the final corrected sentence output, thus to improve speech recognition output results recognition rate. Experiments show that the proposed method improved from 48.50% to 45.31% and 15.37% to 10.31% in terms of Word Error Rate score and Syllable Error Rate as compared to the speech recognition approach. Keyword-Context Dependent Syllable, Automatic Speech Recognizer, Error Correction, Natural Language Processing * The Author ** The Advisor
Anoop, C. S. "Automatic speech recognition for low-resource Indian languages". Thesis, 2023. https://etd.iisc.ac.in/handle/2005/6195.
Texto completoLibros sobre el tema "Syllable-based ASR"
Stein, Gabriele. Peter Levins’ description of word-formation (1570). Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198807377.003.0008.
Texto completoUffmann, Christian. World Englishes and Phonological Theory. Editado por Markku Filppula, Juhani Klemola y Devyani Sharma. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.32.
Texto completoCapítulos de libros sobre el tema "Syllable-based ASR"
Kim, Byeongchang, Junhwi Choi y Gary Geunbae Lee. "ASR Error Management Using RNN Based Syllable Prediction for Spoken Dialog Applications". En Advances in Parallel and Distributed Computing and Ubiquitous Services, 99–106. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0068-3_12.
Texto completoSchneider, Anne H., Johannes Hellrich y Saturnino Luz. "Word, Syllable and Phoneme Based Metrics Do Not Correlate with Human Performance in ASR-Mediated Tasks". En Advances in Natural Language Processing, 392–99. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10888-9_39.
Texto completoHeslop, Kate. "A Poetry Machine". En Viking Mediologies, 160–84. Fordham University Press, 2022. http://dx.doi.org/10.5422/fordham/9780823298242.003.0007.
Texto completoActas de conferencias sobre el tema "Syllable-based ASR"
Ryu, Hyuksu, Minsu Na y Minhwa Chung. "Pronunciation modeling of loanwords for Korean ASR using phonological knowledge and syllable-based segmentation". En 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015. http://dx.doi.org/10.1109/apsipa.2015.7415308.
Texto completoLiu, Chao-Hong, Chung-Hsien Wu, David Sarwono y Jhing-Fa Wang. "Candidate generation for ASR output error correction using a context-dependent syllable cluster-based confusion matrix". En Interspeech 2011. ISCA: ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-488.
Texto completoHromada, Daniel y Hyungjoong Kim. "Digital Primer Implementation of Human-Machine Peer Learning for Reading Acquisition: Introducing Curriculum 2". En 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004027.
Texto completoQu, Zhongdi, Parisa Haghani, Eugene Weinstein y Pedro Moreno. "Syllable-based acoustic modeling with CTC-SMBR-LSTM". En 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2017. http://dx.doi.org/10.1109/asru.2017.8268932.
Texto completoMoungsri, Decha, Tomoki Koriyama y Takao Kobayashi. "Enhanced F0 generation for GPR-based speech synthesis considering syllable-based prosodic features". En 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282285.
Texto completoSakamoto, Nagisa, Kazumasa Yamamoto y Seiichi Nakagawa. "Combination of syllable based N-gram search and word search for spoken term detection through spoken queries and IV/OOV classification". En 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU). IEEE, 2015. http://dx.doi.org/10.1109/asru.2015.7404795.
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