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Relevant bibliographies by topics / Speech processing systems. Pattern recognition systems

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Academic literature on the topic 'Speech processing systems. Pattern recognition systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Speech processing systems. Pattern recognition systems"

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Järvinen, Kari. "Digital speech processing: Speech coding, synthesis, and recognition." Signal Processing 30, no. 1 (January 1993): 133–34. http://dx.doi.org/10.1016/0165-1684(93)90056-g.

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Mišković, Dragiša, Milan Gnjatović, Perica Štrbac, Branimir Trenkić, Nikša Jakovljević, and Vlado Delić. "Hybrid methodological approach to context-dependent speech recognition." International Journal of Advanced Robotic Systems 14, no. 1 (January 1, 2017): 172988141668713. http://dx.doi.org/10.1177/1729881416687131.

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Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

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Modi, Rohan. "Transcript Anatomization with Multi-Linguistic and Speech Synthesis Features." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1755–58. http://dx.doi.org/10.22214/ijraset.2021.35371.

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Handwriting Detection is a process or potential of a computer program to collect and analyze comprehensible input that is written by hand from various types of media such as photographs, newspapers, paper reports etc. Handwritten Text Recognition is a sub-discipline of Pattern Recognition. Pattern Recognition is refers to the classification of datasets or objects into various categories or classes. Handwriting Recognition is the process of transforming a handwritten text in a specific language into its digitally expressible script represented by a set of icons known as letters or characters. Speech synthesis is the artificial production of human speech using Machine Learning based software and audio output based computer hardware. While there are many systems which convert normal language text in to speech, the aim of this paper is to study Optical Character Recognition with speech synthesis technology and to develop a cost effective user friendly image based offline text to speech conversion system using CRNN neural networks model and Hidden Markov Model. The automated interpretation of text that has been written by hand can be very useful in various instances where processing of great amounts of handwritten data is required, such as signature verification, analysis of various types of documents and recognition of amounts written on bank cheques by hand.

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Dmitriev, V. Ya, T. A. Ignat'eva, and V. P. Pilyavskiy. "Development of Artificial Intelligence and Prospects for Its Application." Economics and Management 27, no. 2 (May 1, 2021): 132–38. http://dx.doi.org/10.35854/1998-1627-2021-2-132-138.

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Aim. To analyze the concept of “artificial intelligence”, to justify the effectiveness of using artificial intelligence technologies.Tasks. To study the conceptual apparatus; to propose and justify the author’s definition of the “artificial intelligence” concept; to describe the technology of speech recognition using artificial intelligence.Methodology. The authors used such general scientific methods of cognition as comparison, deduction and induction, analysis, generalization and systematization.Results. Based on a comparative analysis of the existing conceptual apparatus, it is concluded that there is no single concept of “artificial intelligence”. Each author puts his own vision into it. In this regard, the author’s definition of the “artificial intelligence” concept is formulated. It is determined that an important area of applying artificial intelligence technologies in various fields of activity is speech recognition technology. It is shown that the first commercially successful speech recognition prototypes appeared already by the 1990s, and since the beginning of the 21st century. The great interest in “end-to-end” automatic speech recognition has become obvious. While traditional phonetic approaches have requested pronunciation, acoustic, and language model data, end-to-end models simultaneously consider all components of speech recognition, thereby facilitating the stages of self-learning and development. It is established that a significant increase in the” mental “ capabilities of computer technology and the development of new algorithms have led to new achievements in this direction. These advances are driven by the growing demand for speech recognition.Conclusions. According to the authors, artificial intelligence is a complex of computer programs that duplicate the functions of the human brain, opening up the possibility of informal learning based on big data processing, allowing to solve the problems of pattern recognition (text, image, speech) and the formation of management decisions. Currently, the active development of information and communication technologies and artificial intelligence concepts has led to a wide practical application of intelligent technologies, especially in control systems. The impact of these systems can be found in the work of mobile phones and expert systems, in forecasting and other areas. Among the obstacles to the development of this technology is the lack of accuracy in speech and voice recognition systems in the conditions of sound interference, which is always present in the external environment. However, the recent advances overcome this disadvantage.

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Hickt, L. "Speech and speaker recognition." Signal Processing 13, no. 3 (October 1987): 336–38. http://dx.doi.org/10.1016/0165-1684(87)90137-x.

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Tseng, Juin-Ling. "Intelligent Augmented Reality System based on Speech Recognition." International Journal of Circuits, Systems and Signal Processing 15 (March 18, 2021): 178–86. http://dx.doi.org/10.46300/9106.2021.15.20.

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In general, most of the current augmented reality systems can combine 3D virtual scenes with live reality, and users usually interact with 3D objects of the augmented reality (AR) system through image recognition. Although the image-recognition technology has matured enough to allow users to interact with the system, the interaction process is usually limited by the number of patterns used to identify the image. It is not convenient to handle. To provide a more flexible interactive manipulation mode, this study imports the speech-recognition mechanism that allows users to operate 3D objects in an AR system simply by speech. In terms of implementation, the program uses Unity3D as the main development environment and the AR e-Desk as the main development platform. The AR e-Desk interacts through the identification mechanism of the reacTIVision and its markers. We use Unity3D to build the required 3D virtual scenes and objects in the AR e-Desk and import the Google Cloud Speech suite to the AR e-Desk system to develop the speech-interaction mechanism. Then, the intelligent AR system is developed.

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Puder, Henning, and Gerhard Schmidt. "Applied speech and audio processing." Signal Processing 86, no. 6 (June 2006): 1121–23. http://dx.doi.org/10.1016/j.sigpro.2005.07.034.

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PHINYOMARK, ANGKOON, PORNCHAI PHUKPATTARANONT, and CHUSAK LIMSAKUL. "APPLICATIONS OF VARIANCE FRACTAL DIMENSION: A SURVEY." Fractals 22, no. 01n02 (March 2014): 1450003. http://dx.doi.org/10.1142/s0218348x14500030.

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Chaotic dynamical systems are pervasive in nature and can be shown to be deterministic through fractal analysis. There are numerous methods that can be used to estimate the fractal dimension. Among the usual fractal estimation methods, variance fractal dimension (VFD) is one of the most significant fractal analysis methods that can be implemented for real-time systems. The basic concept and theory of VFD are presented. Recent research and the development of several applications based on VFD are reviewed and explained in detail, such as biomedical signal processing and pattern recognition, speech communication, geophysical signal analysis, power systems and communication systems. The important parameters that need to be considered in computing the VFD are discussed, including the window size and the window increment of the feature, and the step size of the VFD. Directions for future research of VFD are also briefly outlined.

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Ujiie, Yuta, and Kohske Takahashi. "Weaker McGurk Effect for Rubin’s Vase-Type Speech in People With High Autistic Traits." Multisensory Research 34, no. 6 (April 16, 2021): 663–79. http://dx.doi.org/10.1163/22134808-bja10047.

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Abstract While visual information from facial speech modulates auditory speech perception, it is less influential on audiovisual speech perception among autistic individuals than among typically developed individuals. In this study, we investigated the relationship between autistic traits (Autism-Spectrum Quotient; AQ) and the influence of visual speech on the recognition of Rubin’s vase-type speech stimuli with degraded facial speech information. Participants were 31 university students (13 males and 18 females; mean age: 19.2, SD: 1.13 years) who reported normal (or corrected-to-normal) hearing and vision. All participants completed three speech recognition tasks (visual, auditory, and audiovisual stimuli) and the AQ–Japanese version. The results showed that accuracies of speech recognition for visual (i.e., lip-reading) and auditory stimuli were not significantly related to participants’ AQ. In contrast, audiovisual speech perception was less susceptible to facial speech perception among individuals with high rather than low autistic traits. The weaker influence of visual information on audiovisual speech perception in autism spectrum disorder (ASD) was robust regardless of the clarity of the visual information, suggesting a difficulty in the process of audiovisual integration rather than in the visual processing of facial speech.

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CHEN, QINGCAI, XIAOLONG WANG, PENGFEI SU, and YI YAO. "AUTO ADAPTED ENGLISH PRONUNCIATION EVALUATION: A FUZZY INTEGRAL APPROACH." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 01 (February 2008): 153–68. http://dx.doi.org/10.1142/s0218001408006090.

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To evaluate the pronunciation skills of spoken English is one of the key tasks for computer-aided spoken language learning (CALL). While most of the researchers focus on improving the speech recognition techniques to build a reliable evaluation system, another important aspect of this task has been ignored, i.e. the pronunciation evaluation model that integrates both the reliabilities of existing speech processing systems and the learner's pronunciation personalities. To take this aspect into consideration, a Sugeno integral-based evaluation model is introduced in this paper. At first, the English phonemes that are hard to be distinguished (HDP) for Chinese language learners are grouped into different HDP sets. Then, the system reliabilities for distinguishing the phonemes within a HDP set are computed from the standard speech corpus and are integrated with the phoneme recognition results under the Sugeno integral framework. The fuzzy measures are given for each subset of speech segments that contains n occurrences of phonemes within a HDP set. Rather than providing a quantity of scores, the linguistic descriptions of evaluation results are given by the model, which is more helpful for the users to improve their spoken language skills. To get a better performance, generic algorithm (GA)-based parameter optimization is also applied to optimize the model parameters. Experiments are conducted on the Sphinx-4 speech recognition platform. They show that, with 84.7% of average recognition rate of the SR system on standard speech corpus, our pronunciation evaluation model has got reasonable and reliable results for three kinds of test corpora.

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Dissertations / Theses on the topic "Speech processing systems. Pattern recognition systems"

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Alphonso, Issac John. "Network training for continuous speech recognition." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-10252003-105104.

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Combrinck, Hendrik Petrus. "A cost, complexity and performance comparison of two automatic language identification architectures." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-12212006-141335/.

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Sundaram, Anand R. K. "Vowel recognition using Kohonen's self-organizing feature maps /." Online version of thesis, 1991. http://hdl.handle.net/1850/10710.

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Sukittanon, Somsak. "Modulation scale analysis : theory and application for nonstationary signal classification /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5875.

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Chen, Xin. "Ensemble methods in large vocabulary continuous speech recognition." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5797.

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Thesis (M.S.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 28, 2008) Vita. Includes bibliographical references.

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Jantan, Adznan Bin. "A comparative study of various analysis techniques for use in speech recognition systems." Thesis, Swansea University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292473.

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Xue, Jian. "Improvement of decoding engine & phonetic decision tree in acoustic modeling for online large vocabulary conversational speech recognition." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4821.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on March 4, 2008) Vita. Includes bibliographical references.

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Chiou, Greg I. "Active contour models for distinct feature tracking and lipreading /." Thesis, Connect to this title online; UW restricted, 1995. http://hdl.handle.net/1773/6023.

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Ravindran, Sourabh. "Physiologically Motivated Methods For Audio Pattern Classification." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14066.

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Human-like performance by machines in tasks of speech and audio processing has remained an elusive goal. In an attempt to bridge the gap in performance between humans and machines there has been an increased effort to study and model physiological processes. However, the widespread use of biologically inspired features proposed in the past has been hampered mainly by either the lack of robustness across a range of signal-to-noise ratios or the formidable computational costs. In physiological systems, sensor processing occurs in several stages. It is likely the case that signal features and biological processing techniques evolved together and are complementary or well matched. It is precisely for this reason that modeling the feature extraction processes should go hand in hand with modeling of the processes that use these features. This research presents a front-end feature extraction method for audio signals inspired by the human peripheral auditory system. New developments in the field of machine learning are leveraged to build classifiers to maximize the performance gains afforded by these features. The structure of the classification system is similar to what might be expected in physiological processing. Further, the feature extraction and classification algorithms can be efficiently implemented using the low-power cooperative analog-digital signal processing platform. The usefulness of the features is demonstrated for tasks of audio classification, speech versus non-speech discrimination, and speech recognition. The low-power nature of the classification system makes it ideal for use in applications such as hearing aids, hand-held devices, and surveillance through acoustic scene monitoring

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Du, Toit A. (Andre). "Automatic classification of spoken South African English variants using a transcription-less speech recognition approach." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/49866.

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Thesis (MEng)--University of Stellenbosch, 2004.
ENGLISH ABSTRACT: We present the development of a pattern recognition system which is capable of classifying different Spoken Variants (SVs) of South African English (SAE) using a transcriptionless speech recognition approach. Spoken Variants (SVs) allow us to unify the linguistic concepts of accent and dialect from a pattern recognition viewpoint. The need for the SAE SV classification system arose from the multi-linguality requirement for South African speech recognition applications and the costs involved in developing such applications.
AFRIKAANSE OPSOMMING: Ons beskryf die ontwikkeling van 'n patroon herkenning stelsel wat in staat is om verskillende Gesproke Variante (GVe) van Suid Afrikaanse Engels (SAE) te klassifiseer met behulp van 'n transkripsielose spraak herkenning metode. Gesproke Variante (GVe) stel ons in staat om die taalkundige begrippe van aksent en dialek te verenig vanuit 'n patroon her kenning oogpunt. Die behoefte aan 'n SAE GV klassifikasie stelsel het ontstaan uit die meertaligheid vereiste vir Suid Afrikaanse spraak herkenning stelsels en die koste verbonde aan die ontwikkeling van sodanige stelsels.

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Books on the topic "Speech processing systems. Pattern recognition systems"

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Rabiner, Lawrence R. Fundamentals of speech recognition. Englewood Cliffs, N.J: PTR Prentice Hall, 1993.

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Ponting, Keith. Computational Models of Speech Pattern Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999.

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Iberoamerican Congress on Pattern Recognition (8th 2003 Havana, Cuba). Progress in pattern recognition, speech and image analysis: 8th Iberoamerican Congress on pattern recognition, CIARP 2003, Havana, Cuba, November 26-29, 2003 : proceedings. New York: Springer, 2003.

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F, Casacuberta, and Sanfeliu Alberto, eds. Advances in pattern recognition and applications: Selected papers from the Vth Spanish Symposium on Pattern Recognition and Image Analysis, 21-25 September 1992. Singapore: World Scientific, 1994.

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Gottuk, Daniel T. Video Image Detection Systems Installation Performance Criteria. New York, NY: Springer New York, 2008.

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Zhang, David. 3D Biometrics: Systems and Applications. New York, NY: Springer New York, 2013.

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Speech recognition for the health professions: (using Dragon NaturallySpeaking). Upper Saddle River, N.J: Pearson/Prentice Hall, 2005.

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Schmitt, Alexander. Towards Adaptive Spoken Dialog Systems. New York, NY: Springer New York, 2013.

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Rao, K. Sreenivasa. Robust Emotion Recognition using Spectral and Prosodic Features. New York, NY: Springer New York, 2013.

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Kovačević, Branko. Adaptive Digital Filters. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Speech processing systems. Pattern recognition systems"

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Cerisara, Christophe. "Dealing with Loss of Synchronism in Multi-Band Continuous Speech Recognition Systems." In Computational Models of Speech Pattern Processing, 90–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_9.

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Abe, Jair Minoro, and Kazumi Nakamatsu. "Paraconsistent Artificial Neural Networks and Pattern Recognition: Speech Production Recognition and Cephalometric Analysis." In Advances in Reasoning-Based Image Processing Intelligent Systems, 365–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24693-7_12.

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Kuchczyński, Marcin, Aleksandra Pawlicka, Marek Pawlicki, and Michał Choraś. "Using Machine Learning to Detect the Signs of Radicalization and Hate Speech on Twitter." In Progress in Image Processing, Pattern Recognition and Communication Systems, 210–18. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81523-3_21.

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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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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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Lefèvre, Fabrice, Claude Montacié, and Marie-José Caraty. "K-Nearest Neighbours Estimator in a HMM-Based Recognition System." In Computational Models of Speech Pattern Processing, 96–101. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_10.

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Mahadevaswamy, U. B., M. Shashank Rao, S. Vrushab, C. Anagha, and V. Sangameshwar. "Visual Speech Processing and Recognition." In Advances in Intelligent Systems and Computing, 481–91. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3383-9_44.

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Sinha, Shweta, Aruna Jain, and Shyam S. Agrawal. "Speech Processing for Hindi Dialect Recognition." In Advances in Intelligent Systems and Computing, 161–69. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04960-1_14.

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Man, K. F., K. S. Tang, and S. Kwong. "Genetic Algorithms in Speech Recognition Systems." In Advanced Textbooks in Control and Signal Processing, 199–257. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0577-0_8.

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Beg, Azam, and S. K. Hasnain. "A Speech Recognition System for Urdu Language." In Wireless Networks, Information Processing and Systems, 118–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89853-5_14.

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Conference papers on the topic "Speech processing systems. Pattern recognition systems"

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Samborski, R., M. Ziółko, B. Ziółko, and J. Gałka. "Speech Extraction from Jammed Signals in Dual-Microphone Systems." In Signal Processing, Pattern Recognition and Applications. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.678-072.

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Lee, Sangkil, Jieun Kim, and Insung Lee. "Speech/Audio Signal Classification Using Spectral Flux Pattern Recognition." In 2012 IEEE Workshop on Signal Processing Systems (SiPS). IEEE, 2012. http://dx.doi.org/10.1109/sips.2012.36.

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Tihomirova, Tamara A., and Leonid M. Tsibulkin. "Multichannel image processing in limited optical-digital pattern recognition systems." In Twenty-Third International Congress on High-Speed Photography and Photonics, edited by Valentina P. Degtyareva, Mikhail A. Monastyrski, Mikhail Y. Schelev, and Alexander V. Smirnov. SPIE, 1999. http://dx.doi.org/10.1117/12.350523.

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Ramachandran, Arr, Sun, and Ritchie. "A pattern recognition and feature fusion formulation for vehicle reidentification in intelligent transportation systems." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004755.

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Priya, E. L. Dhivya, and N. N. Pragash. "Advanced high speed optical pattern recognition for sur-veillance systems." In 2017 Fourth International Conference on Signal Processing,Communication and Networking (ICSCN). IEEE, 2017. http://dx.doi.org/10.1109/icscn.2017.8085723.

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Lei, Haiju, Dehua Li, Hanping Hu, and Zhaonan Guo. "High-speed aerial image processing system based on DSP." In Multispectral Image Processing and Pattern Recognition, edited by Xubang Shen and Jianguo Liu. SPIE, 2001. http://dx.doi.org/10.1117/12.441686.

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D’Souza, Noel M., Jayasimha Atulasimha, and Supriyo Bandyopadhyay. "Four-State Straintronics: Extremely Low Power Nanomagnetic Logic Using Multiferroics With Biaxial Anisotropy." In ASME 2011 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2011. http://dx.doi.org/10.1115/smasis2011-5242.

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The authors had previously theoretically demonstrated that multiferroic nanomagnetic logic can be clocked in ∼1 GHz with few 100 kT/bit power dissipation which is ∼3 orders of magnitude more energy efficient than current CMOS transistor technology that dissipates several 100,000 kT/bit.. In this work, we propose the more novel concept of 4-state logic by numerically demonstrating the feasibity of an ultra low-power 4-state NOR logic gate using multiferroic nanomagnets with biaxial magnetocrystalline anisotropy. Here, the logic bits are encoded in the magnetization orientation of a nanoscale magnetostrictive layer elastically coupled to a piezoelectric layer. The piezoelectric layer can be clocked with a small electrostatic potential (∼0.2 V) to switch the magnetization of the magnetic layer. We also address logic propagation, where the accurate and unidirectional transfer of data from an input nanomagnet along an array of nanomagnets is needed. This is accomplished by devising an effective clocking scheme to the nanomagnet array, which allows for the realization of feasible logic circuits. Ultimately, this technology would enable higher order information processing, such as pattern recognition, to be performed in parallel at very high speeds while consuming extremely low power. Potential applications include high-density logic circuits, associative memory and neuromorphic computing.

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Kumar, T. Lalith, T. Kishore Kumar, and K. Soundar Rajan. "Speech Recognition Using Neural Networks." In 2009 International Conference on Signal Processing Systems. IEEE, 2009. http://dx.doi.org/10.1109/icsps.2009.51.

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Ye, Hong, Youzheng Zhang, and Jianwei Shen. "Study on Speech Recognition of Greeting Based on Biomimetic Pattern Recognition." In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA). IEEE, 2010. http://dx.doi.org/10.1109/iwisa.2010.5473780.

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Zhang, Haitao, Mali Gong, Dazun Zhao, Ping Yan, Ruizhen Cui, and Weipu Jia. "Superresolution techniques in optoelectronic imaging systems." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441368.

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