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Artykuły w czasopismach na temat "Robust speech features"
Huang, Kuo-Chang, Yau-Tarng Juang i Wen-Chieh Chang. "Robust integration for speech features". Signal Processing 86, nr 9 (wrzesień 2006): 2282–88. http://dx.doi.org/10.1016/j.sigpro.2005.10.020.
Pełny tekst źródłaPotamianos, Alexandros. "Novel features for robust speech recognition". Journal of the Acoustical Society of America 112, nr 5 (listopad 2002): 2278. http://dx.doi.org/10.1121/1.4779131.
Pełny tekst źródłaGoh, Yeh Huann, Paramesran Raveendran i Sudhanshu Shekhar Jamuar. "Robust speech recognition using harmonic features". IET Signal Processing 8, nr 2 (kwiecień 2014): 167–75. http://dx.doi.org/10.1049/iet-spr.2013.0094.
Pełny tekst źródłaEskikand, Parvin Zarei, i Seyyed Ali Seyyedsalehia. "Robust speech recognition by extracting invariant features". Procedia - Social and Behavioral Sciences 32 (2012): 230–37. http://dx.doi.org/10.1016/j.sbspro.2012.01.034.
Pełny tekst źródłaDimitriadis, D., P. Maragos i A. Potamianos. "Robust AM-FM features for speech recognition". IEEE Signal Processing Letters 12, nr 9 (wrzesień 2005): 621–24. http://dx.doi.org/10.1109/lsp.2005.853050.
Pełny tekst źródłaHarding, Philip, i Ben Milner. "Reconstruction-based speech enhancement from robust acoustic features". Speech Communication 75 (grudzień 2015): 62–75. http://dx.doi.org/10.1016/j.specom.2015.09.011.
Pełny tekst źródłaRaj, Bhiksha, Michael L. Seltzer i Richard M. Stern. "Reconstruction of missing features for robust speech recognition". Speech Communication 43, nr 4 (wrzesień 2004): 275–96. http://dx.doi.org/10.1016/j.specom.2004.03.007.
Pełny tekst źródłaONOE, K., S. SATO, S. HOMMA, A. KOBAYASHI, T. IMAI i T. TAKAGI. "Bi-Spectral Acoustic Features for Robust Speech Recognition". IEICE Transactions on Information and Systems E91-D, nr 3 (1.03.2008): 631–34. http://dx.doi.org/10.1093/ietisy/e91-d.3.631.
Pełny tekst źródłaBansal, Poonam, Amita Dev i Shail Jain. "Robust Feature Vector Set Using Higher Order Autocorrelation Coefficients". International Journal of Cognitive Informatics and Natural Intelligence 4, nr 4 (październik 2010): 37–46. http://dx.doi.org/10.4018/ijcini.2010100103.
Pełny tekst źródłaMajeed, Sayf A., Hafizah Husain i Salina A. Samad. "Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition". Archives of Acoustics 40, nr 1 (1.03.2015): 25–31. http://dx.doi.org/10.1515/aoa-2015-0004.
Pełny tekst źródłaRozprawy doktorskie na temat "Robust speech features"
Saenko, Ekaterina 1976. "Articulatory features for robust visual speech recognition". Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28736.
Pełny tekst źródłaIncludes bibliographical references (p. 99-105).
This thesis explores a novel approach to visual speech modeling. Visual speech, or a sequence of images of the speaker's face, is traditionally viewed as a single stream of contiguous units, each corresponding to a phonetic segment. These units are defined heuristically by mapping several visually similar phonemes to one visual phoneme, sometimes referred to as a viseme. However, experimental evidence shows that phonetic models trained from visual data are not synchronous in time with acoustic phonetic models, indicating that visemes may not be the most natural building blocks of visual speech. Instead, we propose to model the visual signal in terms of the underlying articulatory features. This approach is a natural extension of feature-based modeling of acoustic speech, which has been shown to increase robustness of audio-based speech recognition systems. We start by exploring ways of defining visual articulatory features: first in a data-driven manner, using a large, multi-speaker visual speech corpus, and then in a knowledge-driven manner, using the rules of speech production. Based on these studies, we propose a set of articulatory features, and describe a computational framework for feature-based visual speech recognition. Multiple feature streams are detected in the input image sequence using Support Vector Machines, and then incorporated in a Dynamic Bayesian Network to obtain the final word hypothesis. Preliminary experiments show that our approach increases viseme classification rates in visually noisy conditions, and improves visual word recognition through feature-based context modeling.
by Ekaterina Saenko.
S.M.
Domont, Xavier. "Hierarchical spectro-temporal features for robust speech recognition". Münster Verl.-Haus Monsenstein und Vannerdat, 2009. http://d-nb.info/1001282655/04.
Pełny tekst źródłaJavadi, Ailar. "Bio-inspired noise robust auditory features". Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44801.
Pełny tekst źródłaSchädler, Marc René [Verfasser]. "Robust automatic speech recognition and modeling of auditory discrimination experiments with auditory spectro-temporal features / Marc René Schädler". Oldenburg : BIS-Verlag, 2016. http://d-nb.info/1113296755/34.
Pełny tekst źródłaJancovic, Peter. "Combination of multiple feature streams for robust speech recognition". Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268386.
Pełny tekst źródłaFairhurst, Harry. "Robust feature extraction for the recognition of noisy speech". Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327705.
Pełny tekst źródłaDarch, Jonathan J. A. "Robust acoustic speech feature prediction from Mel frequency cepstral coefficients". Thesis, University of East Anglia, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445206.
Pełny tekst źródłaSzymanski, Lech. "Comb filter decomposition feature extraction for robust automatic speech recognition". Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27051.
Pełny tekst źródłaSklar, Alexander Gabriel. "Channel Modeling Applied to Robust Automatic Speech Recognition". Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_theses/87.
Pełny tekst źródłaMushtaq, Aleem. "An integrated approach to feature compensation combining particle filters and Hidden Markov Models for robust speech recognition". Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/48982.
Pełny tekst źródłaKsiążki na temat "Robust speech features"
Rao, K. Sreenivasa. Robust Emotion Recognition using Spectral and Prosodic Features. New York, NY: Springer New York, 2013.
Znajdź pełny tekst źródłaRao, K. Sreenivasa, i Shashidhar G. Koolagudi. Robust Emotion Recognition using Spectral and Prosodic Features. Springer, 2013.
Znajdź pełny tekst źródłaRao, K. Sreenivasa, i Shashidhar G. Koolagudi. Robust Emotion Recognition using Spectral and Prosodic Features. Springer, 2013.
Znajdź pełny tekst źródłaCzęści książek na temat "Robust speech features"
Buckow, Jan, Volker Warnke, Richard Huber, Anton Batliner, Elmar Nöth i Heinrich Niemann. "Fast and Robust Features for Prosodic Classification?" W Text, Speech and Dialogue, 193–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48239-3_35.
Pełny tekst źródłaManchala, Sadanandam, i V. Kamakshi Prasad. "GMM Based Language Identification System Using Robust Features". W Speech and Computer, 154–61. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01931-4_21.
Pełny tekst źródłaSchukat-Talamazzini, E. Günter. "Robust Features for Word Recognition". W Recent Advances in Speech Understanding and Dialog Systems, 291–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83476-9_28.
Pełny tekst źródłaMihelič, France, i Janez Žibert. "Robust Speech Detection Based on Phoneme Recognition Features". W Text, Speech and Dialogue, 455–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_57.
Pełny tekst źródłaMissaoui, Ibrahim, i Zied Lachiri. "Gabor Filterbank Features for Robust Speech Recognition". W Lecture Notes in Computer Science, 665–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07998-1_76.
Pełny tekst źródłaMitra, Vikramjit, Horacio Franco, Richard M. Stern, Julien van Hout, Luciana Ferrer, Martin Graciarena, Wen Wang, Dimitra Vergyri, Abeer Alwan i John H. L. Hansen. "Robust Features in Deep-Learning-Based Speech Recognition". W New Era for Robust Speech Recognition, 187–217. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64680-0_8.
Pełny tekst źródłaKovács, György, László Tóth i Tamás Grósz. "Robust Multi-Band ASR Using Deep Neural Nets and Spectro-temporal Features". W Speech and Computer, 386–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_48.
Pełny tekst źródłaEkpenyong, Moses E., Udoinyang G. Inyang i Victor E. Ekong. "Intelligent Speech Features Mining for Robust Synthesis System Evaluation". W Human Language Technology. Challenges for Computer Science and Linguistics, 3–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93782-3_1.
Pełny tekst źródłaAlam, Md Jahangir, Patrick Kenny i Douglas O’Shaughnessy. "Smoothed Nonlinear Energy Operator-Based Amplitude Modulation Features for Robust Speech Recognition". W Advances in Nonlinear Speech Processing, 168–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38847-7_22.
Pełny tekst źródłaMüller, Florian, i Alfred Mertins. "Robust Features for Speaker-Independent Speech Recognition Based on a Certain Class of Translation-Invariant Transformations". W Advances in Nonlinear Speech Processing, 111–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11509-7_15.
Pełny tekst źródłaStreszczenia konferencji na temat "Robust speech features"
Kemp, Thomas, Climent Nadeu, Yin Hay Lam i Josep Maria Sola i. Caros. "Environmental robust features for speech detection". W Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-349.
Pełny tekst źródłaKristjansson, Trausti, Sabine Deligne i Peder Olsen. "Voicing features for robust speech detection". W Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-186.
Pełny tekst źródłaSam, Sethserey, Xiong Xiao, Laurent Besacier, Eric Castelli, Haizhou Li i Eng Siong Chng. "Speech modulation features for robust nonnative speech accent detection". W Interspeech 2011. ISCA: ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-629.
Pełny tekst źródłaKelly, Finnian, i Naomi Harte. "Auditory Features Revisited for Robust Speech Recognition". W 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.1082.
Pełny tekst źródłaMak, Brian, Yik-Cheung Tam i Qi Li. "Discriminative auditory features for robust speech recognition". W Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743734.
Pełny tekst źródłaMak, Yik-Cheung Tam i Qi Li. "Discriminative auditory features for robust speech recognition". W IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005756.
Pełny tekst źródłaSaenko, Kate, Trevor Darrell i James R. Glass. "Articulatory features for robust visual speech recognition". W the 6th international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1027933.1027960.
Pełny tekst źródłaSun, Zhaomang, Fei Zhou i Qingmin Liao. "A robust feature descriptor based on multiple gradient-related features". W 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952388.
Pełny tekst źródłaZha, Zhuan-ling, Jin Hu, Qing-ran Zhan, Ya-hui Shan, Xiang Xie, Jing Wang i Hao-bo Cheng. "Robust speech recognition combining cepstral and articulatory features". W 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322773.
Pełny tekst źródłaDrugman, Thomas, Yannis Stylianou, Langzhou Chen, Xie Chen i Mark J. F. Gales. "Robust excitation-based features for Automatic Speech Recognition". W ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178855.
Pełny tekst źródłaRaporty organizacyjne na temat "Robust speech features"
Nahamoo, David. Robust Models and Features for Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, marzec 1998. http://dx.doi.org/10.21236/ada344834.
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