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Добірка наукової літератури з теми "Speech processing systems"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 30 липня 2024

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Статті в журналах з теми "Speech processing systems"

1

Ibragimova, Sayora. "THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS." Technical Sciences 4, no. 3 (March 30, 2021): 37–41. http://dx.doi.org/10.26739/2181-9696-2021-3-6.

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This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performan

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2

Dasarathy, Belur V. "Robust speech processing." Information Fusion 5, no. 2 (June 2004): 75. http://dx.doi.org/10.1016/j.inffus.2004.02.002.

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3

Thompson, Laura A., and William C. Ogden. "Visible speech improves human language understanding: Implications for speech processing systems." Artificial Intelligence Review 9, no. 4-5 (October 1995): 347–58. http://dx.doi.org/10.1007/bf00849044.

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4

Scott, Sophie K., and Carolyn McGettigan. "The neural processing of masked speech." Hearing Research 303 (September 2013): 58–66. http://dx.doi.org/10.1016/j.heares.2013.05.001.

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5

M Tasbolatov, N. Mekebayev, O. Mamyrbayev, M. Turdalyuly, D. Oralbekova,. "Algorithms and architectures of speech recognition systems." Psychology and Education Journal 58, no. 2 (February 20, 2021): 6497–501. http://dx.doi.org/10.17762/pae.v58i2.3182.

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Digital processing of speech signal and the voice recognition algorithm is very important for fast and accurate automatic scoring of the recognition technology. A voice is a signal of infinite information. The direct analysis and synthesis of a complex speech signal is due to the fact that the information is contained in the signal. Speech is the most natural way of communicating people. The task of speech recognition is to convert speech into a sequence of words using a computer program. This article presents an algorithm of extracting MFCC for speech recognition. The MFCC algorit

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6

Delic, Vlado, Darko Pekar, Radovan Obradovic, and Milan Secujski. "Speech signal processing in ASR&TTS algorithms." Facta universitatis - series: Electronics and Energetics 16, no. 3 (2003): 355–64. http://dx.doi.org/10.2298/fuee0303355d.

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Speech signal processing and modeling in systems for continuous speech recognition and Text-to-Speech synthesis in Serbian language are described in this paper. Both systems are fully developed by the authors and do not use any third party software. Accuracy of the speech recognizer and intelligibility of the TTS system are in the range of the best solutions in the world, and all conditions are met for commercial use of these solutions.

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7

FUNAKOSHI, KOTARO, TAKENOBU TOKUNAGA, and HOZUMI TANAKA. "Processing Japanese Self-correction in Speech Dialog Systems." Journal of Natural Language Processing 10, no. 4 (2003): 33–53. http://dx.doi.org/10.5715/jnlp.10.4_33.

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8

Hills, A., and K. Scott. "Perceived degradation effects in packet speech systems." IEEE Transactions on Acoustics, Speech, and Signal Processing 35, no. 5 (May 1987): 699–701. http://dx.doi.org/10.1109/tassp.1987.1165187.

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9

Gransier, Robin, and Jan Wouters. "Neural auditory processing of parameterized speech envelopes." Hearing Research 412 (December 2021): 108374. http://dx.doi.org/10.1016/j.heares.2021.108374.

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10

Moon, Todd K., Jacob H. Gunther, Cortnie Broadus, Wendy Hou, and Nils Nelson. "Turbo Processing for Speech Recognition." IEEE Transactions on Cybernetics 44, no. 1 (January 2014): 83–91. http://dx.doi.org/10.1109/tcyb.2013.2247593.

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Дисертації з теми "Speech processing systems"

1

Coetzee, H. J. "The development of a new objective speech quality measure for speech coding applications." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/15474.

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2

Morris, Robert W. "Enhancement and recognition of whispered speech." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180338/unrestricted/morris%5frobert%5fw%5f200312%5fphd.pdf.

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3

Quackenbush, Schuyler Reynier. "Objective measures of speech quality." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/13376.

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4

Lucey, Simon. "Audio-visual speech processing." Thesis, Queensland University of Technology, 2002. https://eprints.qut.edu.au/36172/7/SimonLuceyPhDThesis.pdf.

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Speech is inherently bimodal, relying on cues from the acoustic and visual speech modalities for perception. The McGurk effect demonstrates that when humans are presented with conflicting acoustic and visual stimuli, the perceived sound may not exist in either modality. This effect has formed the basis for modelling the complementary nature of acoustic and visual speech by encapsulating them into the relatively new research field of audio-visual speech processing (AVSP). Traditional acoustic based speech processing systems have attained a high level of performance in recent years, but the p

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5

Chiou, Fred Y. "User-interactive speech enhancement using fuzzy logic." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/14916.

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6

陳我智 and Ngor-chi Chan. "Text-to-speech conversion for Putonghua." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31209580.

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7

Barger, Peter James. "Speech processing for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36081/1/36081_Barger_1998.pdf.

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This thesis examines speech processing systems appropriate for use in forensic analysis. The need for automatic speech processing systems for forensic use is justified by the increasing use of electronically recorded speech for communication. An automatic speaker identification and verification system is described which was tested on data gathered by the Queensland Police Force. Speaker identification using Gaussian mixture models (GMMs) is shown to be useful as an indicator of identity, but not sufficiently accurate to be used as the sole means of identification. It is shown that training G

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8

Yatrou, Paul M. "Analysis of predictor mistracking in ADPCM speech coders." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66242.

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9

Fang, Jie. "Design of secure speech encryption systems." Thesis, Queensland University of Technology, 1990. https://eprints.qut.edu.au/36471/1/36471_Fang_1990.pdf.

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This thesis investigates the design of digital speech encryption systems based on low bit rate vocoders. The speech quality and the cryptographic strength of the system are determined by vocoder and encryptor respectively. Three different low bit rate vocoders, 2400 BPS LPC ( Linear Prediction Coding) vocoder, 9600 BPS MELPC (Mul tipulse Excited Linear Prediction Coding) vocoder and 4800 BPS CELP (Codebook Excited Linear Prediction coding) vocoder, have been simulated. The performances of these vocoders are evaluated by using four objective measures. The thesis considers the follows

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10

Liu, Zhu Lin. "Speech synthesis via adaptive Fourier decomposition." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2493215.

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Книги з теми "Speech processing systems"

1

Sinha, Priyabrata. Speech Processing in Embedded Systems. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-75581-6.

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2

W, Parsons Thomas. Voice and speech processing. New York: McGraw-Hill, 1986.

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3

Wang, Jhing-Fa. Real World Speech Processing. Boston, MA: Springer US, 2004.

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4

Frank, Fallside, and Woods William A, eds. Computer speech processing. Englewood Cliffs, (N.J.): Prentice-Hall, 1985.

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5

Tetschner, Walt. Voice processing. 2nd ed. Boston: Artech House, 1993.

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6

Rabiner, Lawrence R. Introduction to digital speech processing. Boston, Mass: Now, 2007.

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7

Rabiner, Lawrence R. Introduction to digital speech processing. Boston, Massachusetts: Now, 2007.

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8

Rabiner, Lawrence R. Introduction to digital speech processing. Boston, Mass: Now, 2007.

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9

Sabourin, Conrad. Computational speech processing: Bibliography. Montréal, Qué: Infolingua, 1994.

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10

Sabourin, Conrad. Computational speech processing: Bibliography. Montréal: Infolingua Inc., 1994.

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Частини книг з теми "Speech processing systems"

1

Frerking, Marvin E. "Speech Processing." In Digital Signal Processing in Communication Systems, 490–547. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4757-4990-8_9.

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2

Néel, Françoise D., and Wolfgang M. Minker. "Multimodal Speech Systems." In Computational Models of Speech Pattern Processing, 404–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_34.

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3

Sinha, Priyabrata. "Speech Recognition." In Speech Processing in Embedded Systems, 143–55. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_10.

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4

Sinha, Priyabrata. "Speech Synthesis." In Speech Processing in Embedded Systems, 157–64. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_11.

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5

Quast, Holger, and Robert Bosch. "Speech Dialogue Systems and Natural Language Processing." In Computer Speech, 67–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-06384-2_4.

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6

Sinha, Priyabrata. "Basic Speech Processing Concepts." In Speech Processing in Embedded Systems, 37–54. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_3.

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7

Sinha, Priyabrata. "Peripherals for Speech Processing." In Speech Processing in Embedded Systems, 75–91. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_5.

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8

Sinha, Priyabrata. "Signal Processing Fundamentals." In Speech Processing in Embedded Systems, 9–36. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_2.

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9

Sinha, Priyabrata. "Speech Compression Overview." In Speech Processing in Embedded Systems, 93–100. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-75581-6_6.

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10

Suendermann, David, and Roberto Pieraccini. "Crowdsourcing for Industrial Spoken Dialog Systems." In Crowdsourcing for Speech Processing, 280–302. Oxford, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118541241.ch10.

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Тези доповідей конференцій з теми "Speech processing systems"

1

"Signal Processing. Speech and Audio Processing." In 2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2022. http://dx.doi.org/10.1109/iwssip55020.2022.9854416.

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2

"Session WA2b: Speech processing." In 2017 51st Asilomar Conference on Signals, Systems, and Computers. IEEE, 2017. http://dx.doi.org/10.1109/acssc.2017.8335704.

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3

"Session WA8b1 Speech Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399566.

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4

Maina, Ciira wa, and John MacLaren Walsh. "Approximate Bayesian robust speech processing." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190027.

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5

Nguyen Thanh Chung and Tang Jung Low. "Speech processing in Java-based PC speech commanding application." In 2007 International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2007. http://dx.doi.org/10.1109/icias.2007.4658590.

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6

Hansen, John H. L., Jay Plucienkowski, Stephen Gallant, Bryan Pellom, and Wayne Ward. "CU-move: robust speech processing for in-vehicle speech systems." In 6th International Conference on Spoken Language Processing (ICSLP 2000). ISCA: ISCA, 2000. http://dx.doi.org/10.21437/icslp.2000-130.

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Katharina Fuchs, Anna, Clemens Amon, and Martin Hagmüller. "Speech/Non-Speech Detection for Electro-Larynx Speech Using EMG." In International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005181401380144.

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Ogunfunmi, Tokunbo. "Session TP7b: Speech processing and speech recognition (invited) [breaker page]." In 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489301.

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Black, Alan W., Ralf D. Brown, Robert Frederking, Kevin Lenzo, John Moody, Alexander I. Rudnicky, Rita Singh, and Eric Steinbrecher. "Rapid development of speech-to-speech translation systems." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA: ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-504.

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"Session WA6a: Speech processing I." In 2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers. IEEE, 2009. http://dx.doi.org/10.1109/acssc.2009.5469739.

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Звіти організацій з теми "Speech processing systems"

1

Furey, John, Austin Davis, and Jennifer Seiter-Moser. Natural language indexing for pedoinformatics. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41960.

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The multiple schema for the classification of soils rely on differing criteria but the major soil science systems, including the United States Department of Agriculture (USDA) and the international harmonized World Reference Base for Soil Resources soil classification systems, are primarily based on inferred pedogenesis. Largely these classifications are compiled from individual observations of soil characteristics within soil profiles, and the vast majority of this pedologic information is contained in nonquantitative text descriptions. We present initial text mining analyses of parsed text i

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2

Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platfor

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3

Bilgutay, Nihat M. Computer Facilities for High-Speed Data Acquisition, Signal Processing and Large Scale System Simulation. Fort Belvoir, VA: Defense Technical Information Center, June 1986. http://dx.doi.org/10.21236/ada170935.

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4

Wada, Yasutaka. Working Paper PUEAA No. 3. Parallel Processing and Parallelizing Compilation Techniques for "Green Computing". Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.001r.2022.

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The fourth technological revolution has brought great advances in manufacturing processes and human communications. Although processors have become increasingly efficient, both in speed, capacity and energy consumption, their functionality regarding this last point has yet to improve. The latest innovations represent an opportunity to create "green computing" and not only more environmentally friendly electronics and software, but also to use their new efficiency to improve our daily activities, as well as the designs of our cities themselves to make them more environmentally sustainable. Thes

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5

Lane, Lerose, and DingXin Cheng. Pavement Condition Survey using Drone Technology. Mineta Transportation Institute, June 2023. http://dx.doi.org/10.31979/mti.2023.2202.

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Timely repairs of pavement defects are essential in protecting both public road and highway systems. Identification of pavement distresses is necessary for planning pavement repairs. This has previously been performed by engineers surveying the roadways visually in the field. As drone usage has progressed, it has become clear that drones are a valuable tool to enhance visual documentation, improve project communication, and provide various data for processing. The use of drone technology has improved both the speed and accuracy of capturing data. Available software has allowed the data to be p

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6

Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM.

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7

Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wave

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8

Murphy, Keire, and Anne Sheridan. Annual report on migration and asylum 2022: Ireland. ESRI, November 2023. http://dx.doi.org/10.26504/sustat124.

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Annual Report on Migration and Asylum gives overview of statistics and developments in migration in 2022. The European Migration Network (EMN) Ireland within the ESRI has published its annual review of migration and asylum in Ireland. The EMN is an EU network that provides objective, comparable policy-relevant information on migration and international protection. EMN Ireland is located in the ESRI and is funded by the European Union and the Department of Justice. With an overview of the latest data as well as policy and operational developments, research, and case law from 2022, this report i

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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, November 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned

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