Academic literature on the topic 'Robust speech features'
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Journal articles on the topic "Robust speech features"
Huang, Kuo-Chang, Yau-Tarng Juang, and Wen-Chieh Chang. "Robust integration for speech features." Signal Processing 86, no. 9 (September 2006): 2282–88. http://dx.doi.org/10.1016/j.sigpro.2005.10.020.
Full textPotamianos, Alexandros. "Novel features for robust speech recognition." Journal of the Acoustical Society of America 112, no. 5 (November 2002): 2278. http://dx.doi.org/10.1121/1.4779131.
Full textGoh, Yeh Huann, Paramesran Raveendran, and Sudhanshu Shekhar Jamuar. "Robust speech recognition using harmonic features." IET Signal Processing 8, no. 2 (April 2014): 167–75. http://dx.doi.org/10.1049/iet-spr.2013.0094.
Full textEskikand, Parvin Zarei, and 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.
Full textDimitriadis, D., P. Maragos, and A. Potamianos. "Robust AM-FM features for speech recognition." IEEE Signal Processing Letters 12, no. 9 (September 2005): 621–24. http://dx.doi.org/10.1109/lsp.2005.853050.
Full textHarding, Philip, and Ben Milner. "Reconstruction-based speech enhancement from robust acoustic features." Speech Communication 75 (December 2015): 62–75. http://dx.doi.org/10.1016/j.specom.2015.09.011.
Full textRaj, Bhiksha, Michael L. Seltzer, and Richard M. Stern. "Reconstruction of missing features for robust speech recognition." Speech Communication 43, no. 4 (September 2004): 275–96. http://dx.doi.org/10.1016/j.specom.2004.03.007.
Full textONOE, K., S. SATO, S. HOMMA, A. KOBAYASHI, T. IMAI, and T. TAKAGI. "Bi-Spectral Acoustic Features for Robust Speech Recognition." IEICE Transactions on Information and Systems E91-D, no. 3 (March 1, 2008): 631–34. http://dx.doi.org/10.1093/ietisy/e91-d.3.631.
Full textBansal, Poonam, Amita Dev, and Shail Jain. "Robust Feature Vector Set Using Higher Order Autocorrelation Coefficients." International Journal of Cognitive Informatics and Natural Intelligence 4, no. 4 (October 2010): 37–46. http://dx.doi.org/10.4018/ijcini.2010100103.
Full textMajeed, Sayf A., Hafizah Husain, and Salina A. Samad. "Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition." Archives of Acoustics 40, no. 1 (March 1, 2015): 25–31. http://dx.doi.org/10.1515/aoa-2015-0004.
Full textDissertations / Theses on the topic "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.
Full textIncludes 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.
Full textJavadi, Ailar. "Bio-inspired noise robust auditory features." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44801.
Full textSchädler, 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.
Full textJancovic, 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.
Full textFairhurst, 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.
Full textDarch, 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.
Full textSzymanski, Lech. "Comb filter decomposition feature extraction for robust automatic speech recognition." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27051.
Full textSklar, Alexander Gabriel. "Channel Modeling Applied to Robust Automatic Speech Recognition." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_theses/87.
Full textMushtaq, 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.
Full textBooks on the topic "Robust speech features"
Rao, K. Sreenivasa. Robust Emotion Recognition using Spectral and Prosodic Features. New York, NY: Springer New York, 2013.
Find full textRao, K. Sreenivasa, and Shashidhar G. Koolagudi. Robust Emotion Recognition using Spectral and Prosodic Features. Springer, 2013.
Find full textRao, K. Sreenivasa, and Shashidhar G. Koolagudi. Robust Emotion Recognition using Spectral and Prosodic Features. Springer, 2013.
Find full textBook chapters on the topic "Robust speech features"
Buckow, Jan, Volker Warnke, Richard Huber, Anton Batliner, Elmar Nöth, and Heinrich Niemann. "Fast and Robust Features for Prosodic Classification?" In Text, Speech and Dialogue, 193–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48239-3_35.
Full textManchala, Sadanandam, and V. Kamakshi Prasad. "GMM Based Language Identification System Using Robust Features." In Speech and Computer, 154–61. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01931-4_21.
Full textSchukat-Talamazzini, E. Günter. "Robust Features for Word Recognition." In 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.
Full textMihelič, France, and Janez Žibert. "Robust Speech Detection Based on Phoneme Recognition Features." In Text, Speech and Dialogue, 455–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11846406_57.
Full textMissaoui, Ibrahim, and Zied Lachiri. "Gabor Filterbank Features for Robust Speech Recognition." In Lecture Notes in Computer Science, 665–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07998-1_76.
Full textMitra, Vikramjit, Horacio Franco, Richard M. Stern, Julien van Hout, Luciana Ferrer, Martin Graciarena, Wen Wang, Dimitra Vergyri, Abeer Alwan, and John H. L. Hansen. "Robust Features in Deep-Learning-Based Speech Recognition." In 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.
Full textKovács, György, László Tóth, and Tamás Grósz. "Robust Multi-Band ASR Using Deep Neural Nets and Spectro-temporal Features." In Speech and Computer, 386–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11581-8_48.
Full textEkpenyong, Moses E., Udoinyang G. Inyang, and Victor E. Ekong. "Intelligent Speech Features Mining for Robust Synthesis System Evaluation." In 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.
Full textAlam, Md Jahangir, Patrick Kenny, and Douglas O’Shaughnessy. "Smoothed Nonlinear Energy Operator-Based Amplitude Modulation Features for Robust Speech Recognition." In 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.
Full textMüller, Florian, and Alfred Mertins. "Robust Features for Speaker-Independent Speech Recognition Based on a Certain Class of Translation-Invariant Transformations." In 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.
Full textConference papers on the topic "Robust speech features"
Kemp, Thomas, Climent Nadeu, Yin Hay Lam, and Josep Maria Sola i. Caros. "Environmental robust features for speech detection." In Interspeech 2004. ISCA: ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-349.
Full textKristjansson, Trausti, Sabine Deligne, and Peder Olsen. "Voicing features for robust speech detection." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-186.
Full textSam, Sethserey, Xiong Xiao, Laurent Besacier, Eric Castelli, Haizhou Li, and Eng Siong Chng. "Speech modulation features for robust nonnative speech accent detection." In Interspeech 2011. ISCA: ISCA, 2011. http://dx.doi.org/10.21437/interspeech.2011-629.
Full textKelly, Finnian, and Naomi Harte. "Auditory Features Revisited for Robust Speech Recognition." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.1082.
Full textMak, Brian, Yik-Cheung Tam, and Qi Li. "Discriminative auditory features for robust speech recognition." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743734.
Full textMak, Yik-Cheung Tam, and Qi Li. "Discriminative auditory features for robust speech recognition." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005756.
Full textSaenko, Kate, Trevor Darrell, and James R. Glass. "Articulatory features for robust visual speech recognition." In the 6th international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1027933.1027960.
Full textSun, Zhaomang, Fei Zhou, and Qingmin Liao. "A robust feature descriptor based on multiple gradient-related features." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952388.
Full textZha, Zhuan-ling, Jin Hu, Qing-ran Zhan, Ya-hui Shan, Xiang Xie, Jing Wang, and Hao-bo Cheng. "Robust speech recognition combining cepstral and articulatory features." In 2017 3rd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/compcomm.2017.8322773.
Full textDrugman, Thomas, Yannis Stylianou, Langzhou Chen, Xie Chen, and Mark J. F. Gales. "Robust excitation-based features for Automatic Speech Recognition." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178855.
Full textReports on the topic "Robust speech features"
Nahamoo, David. Robust Models and Features for Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada344834.
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