Log in

Relevant bibliographies by topics / Pattern recognition, speech recognition

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Pattern recognition, speech recognition'

Author: Grafiati

Published: 17 September 2021

Last updated: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Pattern recognition, speech recognition.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Pattern recognition, speech recognition"

1

Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition." Applications of Mathematics 30, no. 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Wymore, Ben S. "Dynamic speech recognition pattern switching for enhanced speech recognition accuracy." Journal of the Acoustical Society of America 115, no. 3 (2004): 959. http://dx.doi.org/10.1121/1.1697778.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

CHOU, W., C. H. LEE, B. H. JUANG, and F. K. SOONG. "A MINIMUM ERROR RATE PATTERN RECOGNITION APPROACH TO SPEECH RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (February 1994): 5–31. http://dx.doi.org/10.1142/s0218001494000024.

Full text

Abstract:

In this paper, a minimum error rate pattern recognition approach to speech recognition is studied with particular emphasis on the speech recognizer designs based on hidden Markov models (HMMs) and Viterbi decoding. This approach differs from the traditional maximum likelihood based approach in that the objective of the recognition error rate minimization is established through a specially designed loss function, and is not based on the assumptions made about the speech generation process. Various theoretical and practical issues concerning this minimum error rate pattern recognition approach in speech recognition are investigated. The formulation and the algorithmic structures of several minimum error rate training algorithms for an HMM-based speech recognizer are discussed. The tree-trellis based N-best decoding method and a robust speech recognition scheme based on the combined string models are described. This approach can be applied to large vocabulary, continuous speech recognition tasks and to speech recognizers using word or subword based speech recognition units. Various experimental results have shown that significant error rate reduction can be achieved through the proposed approach.

APA, Harvard, Vancouver, ISO, and other styles

4

Nearey, Terrance M. "Speech perception as pattern recognition." Journal of the Acoustical Society of America 101, no. 6 (June 1997): 3241–54. http://dx.doi.org/10.1121/1.418290.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

De Mori, R. "Knowledge-based speech pattern recognition." Computer Speech & Language 2, no. 3-4 (September 1987): 367–68. http://dx.doi.org/10.1016/0885-2308(87)90020-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Park, Chang-Hyun, and Kwee-Bo Sim. "Pattern Recognition Methods for Emotion Recognition with speech signal." International Journal of Fuzzy Logic and Intelligent Systems 6, no. 2 (June 1, 2006): 150–54. http://dx.doi.org/10.5391/ijfis.2006.6.2.150.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Aucouturier, Jean-Julien, and Laurent Daudet. "Pattern recognition of non-speech audio." Pattern Recognition Letters 31, no. 12 (September 2010): 1487–88. http://dx.doi.org/10.1016/j.patrec.2010.05.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Nearey, Terrance M. "Speech perception as a pattern recognition." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 3334. http://dx.doi.org/10.1121/1.412782.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

CASACUBERTA, FRANCISCO, ENRIQUE VIDAL, ALBERTO SANCHIS, and JUAN-MIGUEL VILAR. "PATTERN RECOGNITION APPROACHES FOR SPEECH-TO-SPEECH TRANSLATION." Cybernetics and Systems 35, no. 1 (January 2004): 3–17. http://dx.doi.org/10.1080/01969720490246812.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Partila, Pavol, Miroslav Voznak, and Jaromir Tovarek. "Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System." Scientific World Journal 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/573068.

Full text

Abstract:

The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks,k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Pattern recognition, speech recognition"

1

Milner, Benjamin Peter. "Speech recognition in adverse environments." Thesis, University of East Anglia, 1994. https://ueaeprints.uea.ac.uk/2907/.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Long, Christopher J. "Wavelet methods in speech recognition." Thesis, Loughborough University, 1999. https://dspace.lboro.ac.uk/2134/14108.

Full text

Abstract:

In this thesis, novel wavelet techniques are developed to improve parametrization of speech signals prior to classification. It is shown that non-linear operations carried out in the wavelet domain improve the performance of a speech classifier and consistently outperform classical Fourier methods. This is because of the localised nature of the wavelet, which captures correspondingly well-localised time-frequency features within the speech signal. Furthermore, by taking advantage of the approximation ability of wavelets, efficient representation of the non-stationarity inherent in speech can be achieved in a relatively small number of expansion coefficients. This is an attractive option when faced with the so-called 'Curse of Dimensionality' problem of multivariate classifiers such as Linear Discriminant Analysis (LDA) or Artificial Neural Networks (ANNs). Conventional time-frequency analysis methods such as the Discrete Fourier Transform either miss irregular signal structures and transients due to spectral smearing or require a large number of coefficients to represent such characteristics efficiently. Wavelet theory offers an alternative insight in the representation of these types of signals. As an extension to the standard wavelet transform, adaptive libraries of wavelet and cosine packets are introduced which increase the flexibility of the transform. This approach is observed to be yet more suitable for the highly variable nature of speech signals in that it results in a time-frequency sampled grid that is well adapted to irregularities and transients. They result in a corresponding reduction in the misclassification rate of the recognition system. However, this is necessarily at the expense of added computing time. Finally, a framework based on adaptive time-frequency libraries is developed which invokes the final classifier to choose the nature of the resolution for a given classification problem. The classifier then performs dimensionaIity reduction on the transformed signal by choosing the top few features based on their discriminant power. This approach is compared and contrasted to an existing discriminant wavelet feature extractor. The overall conclusions of the thesis are that wavelets and their relatives are capable of extracting useful features for speech classification problems. The use of adaptive wavelet transforms provides the flexibility within which powerful feature extractors can be designed for these types of application.

APA, Harvard, Vancouver, ISO, and other styles

3

Stewart, Darryl William. "Syllable based continuous speech recognition." Thesis, Queen's University Belfast, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325993.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Luettin, Juergen. "Visual speech and speaker recognition." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264432.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mwangi, Elijah. "Speaker independent isolated word recognition." Thesis, Loughborough University, 1987. https://dspace.lboro.ac.uk/2134/15425.

Full text

Abstract:

The work presented in this thesis concerns the recognition of isolated words using a pattern matching approach. In such a system, an unknown speech utterance, which is to be identified, is transformed into a pattern of characteristic features. These features are then compared with a set of pre-stored reference patterns that were generated from the vocabulary words. The unknown word is identified as that vocabulary word for which the reference pattern gives the best match. One of the major difficul ties in the pattern comparison process is that speech patterns, obtained from the same word, exhibit non-linear temporal fluctuations and thus a high degree of redundancy. The initial part of this thesis considers various dynamic time warping techniques used for normalizing the temporal differences between speech patterns. Redundancy removal methods are also considered, and their effect on the recognition accuracy is assessed. Although the use of dynamic time warping algorithms provide considerable improvement in the accuracy of isolated word recognition schemes, the performance is ultimately limited by their poor ability to discriminate between acoustically similar words. Methods for enhancing the identification rate among acoustically similar words, by using common pattern features for similar sounding regions, are investigated. Pattern matching based, speaker independent systems, can only operate with a high recognition rate, by using multiple reference patterns for each of the words included in the vocabulary. These patterns are obtained from the utterances of a group of speakers. The use of multiple reference patterns, not only leads to a large increase in the memory requirements of the recognizer, but also an increase in the computational load. A recognition system is proposed in this thesis, which overcomes these difficulties by (i) employing vector quantization techniques to reduce the storage of reference patterns, and (ii) eliminating the need for dynamic time warping which reduces the computational complexity of the system. Finally, a method of identifying the acoustic structure of an utterance in terms of voiced, unvoiced, and silence segments by using fuzzy set theory is proposed. The acoustic structure is then employed to enhance the recognition accuracy of a conventional isolated word recognizer.

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Allerhand, M. H. "A knowledge-based approach to speech pattern recognition." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377200.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Baothman, Fatmah bint Abdul Rahman. "Phonology-based automatic speech recognition for Arabic." Thesis, University of Huddersfield, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273720.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Holmes, Wendy Jane. "Modelling segmental variability for automatic speech recognition." Thesis, University College London (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267859.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Prager, Richard William. "Parallel processing networks for automatic speech recognition." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238443.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Pattern recognition, speech recognition"

1

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

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

name, No. Pattern recognition in speech and language processing. Boca Raton, FL: CRC Press, 2002.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Paulus, Dietrich W. R., and Joachim Hornegger. Pattern Recognition of Images and Speech in C++. Wiesbaden: Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-663-13991-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Sanfeliu, Alberto, and José Ruiz-Shulcloper, eds. Progress in Pattern Recognition, Speech and Image Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b94613.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Paulus, Dietrich W. R. Pattern recognition of images and speech in C++. Wiesbaden: Vieweg, 1997.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Strukturno-lingvisticheskie metody raspoznavanii͡a︡ izobrazheniĭ v realʹnom vremeni. Kiev: Nauk. dumka, 1986.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

service), SpringerLink (Online, ed. Pattern Recognition, Machine Intelligence and Biometrics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Dougherty, Geoff. Pattern Recognition and Classification: An Introduction. New York, NY: Springer New York, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Gerhard, Sagerer, Posch Stefan, and Kummert Franz, eds. Mustererkennung 1995: Verstehen akustischer und visueller Informationen : 17. DAGM-Symposium, Bielefeld, 13.-15. September 1995. Berlin: Springer, 1995.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Pattern recognition, speech recognition"

1

Fink, Gernot A. "Speech Recognition." In Markov Models for Pattern Recognition, 229–36. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6308-4_13.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Bourlard, Hervé A., and Nelson Morgan. "Statistical Pattern Classification." In Connectionist Speech Recognition, 15–25. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-3210-1_2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Paulus, Dietrich W. R., and Joachim Hornegger. "Speech Recognition." In Pattern Recognition of Images and Speech in C++, 329–53. Wiesbaden: Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-663-13991-1_25.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Paulus, Dietrich W. R., and Joachim Hornegger. "Pattern Recognition." In Pattern Recognition of Images and Speech in C++, 5–16. Wiesbaden: Vieweg+Teubner Verlag, 1997. http://dx.doi.org/10.1007/978-3-663-13991-1_1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Höge, Harald, Sascha Hohenner, Bernhard Kämmerer, Niels Kunstmann, Stefanie Schachtl, Martin Schönle, and Panji Setiawan. "Automotive Speech Recognition." In Advances in Pattern Recognition, 347–73. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Gao, Yuqing, Bowen Zhou, Weizhong Zhu, and Wei Zhang. "Handheld Speech to Speech Translation System." In Advances in Pattern Recognition, 327–46. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Furui, Sadaoki. "Robust Speech Recognition." In Computational Models of Speech Pattern Processing, 102–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_11.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Nöth, E., S. Harbeck, and H. Niemann. "Multilingual Speech Recognition." In Computational Models of Speech Pattern Processing, 362–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Pearce, David. "Distributed Speech Recognition Standards." In Advances in Pattern Recognition, 87–106. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-143-5_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Furui, Sadaoki. "Speaker Recognition." In Computational Models of Speech Pattern Processing, 132–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60087-6_14.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Pattern recognition, speech recognition"

1

"CCPR 2008 Keynote Speech 3 and Keynote Speech 4." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.7.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

"CCPR 2008 Keynote Speech 1." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

"CCPR 2008 Keynote Speech 2." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Lu, Bin, and Jing-jing Xu. "Research on Isolated Word Speech Recognition Based on Biomimetic Pattern Recognition." In 2009 International Conference on Artificial Intelligence and Computational Intelligence. IEEE, 2009. http://dx.doi.org/10.1109/aici.2009.371.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Xu, Bo, Cheng Lu, Yandong Guo, and Jacob Wang. "Discriminative Multi-Modality Speech Recognition." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01444.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Leila and G. Chollet. "Efficient Gaussian Mixture for Speech Recognition." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.475.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Qilao, Hasi, and Guang-Lai Gao. "Researching of Speech Recognition Oriented Mongolian Acoustic Model." In 2008 Chinese Conference on Pattern Recognition. IEEE, 2008. http://dx.doi.org/10.1109/ccpr.2008.85.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Fan, Yong, and Christos Davatzikos. "Pattern recognition of functional brain networks." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953370.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Franzini, Simone, and Jezekiel Ben-Arie. "Speech recognition by indexing and sequencing." In 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR). IEEE, 2010. http://dx.doi.org/10.1109/socpar.2010.5686409.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Pattern recognition, speech recognition"

1

Montana, Shaun P. A Statistical Pattern Recognition Tool. Fort Belvoir, VA: Defense Technical Information Center, June 1997. http://dx.doi.org/10.21236/ada327388.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Montana, Shaun P. Statistical Pattern Recognition Tool Upgrades. Fort Belvoir, VA: Defense Technical Information Center, July 1999. http://dx.doi.org/10.21236/ada367916.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Paek, Eung Gi. Optical pattern recognition with microlasers. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

McKenney, B., M. McGrain, A. Klinger, J. Aggarwal, N. George, and R. Haralick. Soviet image pattern recognition research. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/6764408.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Hoeferlin, David M., Brian M. Ore, Stephen A. Thorn, and David Snyder. Speech Processing and Recognition (SPaRe). Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada540142.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Kubala, F., S. Austin, C. Barry, J. Makhoul, P. Placeway, and R. Schwartz. Byblos Speech Recognition Benchmark Results. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada459943.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Pelletier, Justin M. Pattern Recognition Software: Functional Methodology Document. Fort Belvoir, VA: Defense Technical Information Center, January 2009. http://dx.doi.org/10.21236/ada494881.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

George, Nicholas. Optoelectronic Workshops II. Automatic Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada203779.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

MacDonald, Jacqueline A., and Mitchell J. Small. Statistical Methods for UXO Pattern Recognition. Fort Belvoir, VA: Defense Technical Information Center, December 2007. http://dx.doi.org/10.21236/ada603920.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Schwartz, Richard, and Owen Kimball. Toward Real-Time Continuous Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada208196.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!