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Journal articles on the topic 'Speech pattern recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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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.

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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.

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3

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

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4

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

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5

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

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6

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 (1994): 5–31. http://dx.doi.org/10.1142/s0218001494000024.

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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.

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7

Linggard, Robert. "Speech pattern recognition using pattern recognizers and classifiers." Journal of the Acoustical Society of America 105, no. 3 (1999): 1450. http://dx.doi.org/10.1121/1.426674.

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8

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

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9

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

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10

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 (2006): 150–54. http://dx.doi.org/10.5391/ijfis.2006.6.2.150.

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11

Herrera Martínez, Marcelo, Andrea Lorena Aldana Blanco, and Ana Maria Guzmán Palacios. "Speech pattern recognition for forensic acoustic purposes." TECCIENCIA 9, no. 17 (2014): 47–56. http://dx.doi.org/10.18180/tecciencia.2014.17.5.

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12

Nearey, Terrance M. "Explicit pattern recognition models for speech perception." Journal of the Acoustical Society of America 114, no. 4 (2003): 2445. http://dx.doi.org/10.1121/1.4779336.

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13

Maan, A. K., A. P. James, and S. Dimitrijev. "Memristor pattern recogniser: isolated speech word recognition." Electronics Letters 51, no. 17 (2015): 1370–72. http://dx.doi.org/10.1049/el.2015.1428.

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14

AGUILAR, G. "Alaryngeal Speech Enhancement Using Pattern Recognition Techniques." IEICE Transactions on Information and Systems E88-D, no. 7 (2005): 1618–22. http://dx.doi.org/10.1093/ietisy/e88-d.7.1618.

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15

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.

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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.

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16

WANG, HSIAO-CHUAN, and HSIAO-FEN PAI. "RECOGNITION OF MANDARIN SYLLABLES BASED ON THE DISTRIBUTION OF TWO-DIMENSIONAL CEPSTRAL COEFFICIENTS." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (1994): 247–57. http://dx.doi.org/10.1142/s0218001494000127.

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This paper presents a speech recognition method based on the distribution of two-dimensional cepstral (TDC) coefficients. For each recognition unit, a TDC matrix is calculated. A set of selected TDC coefficients forms a pattern to represent this speech segment. By assuming the Gaussian distribution of the TDC coefficients, a statistical model for a class of speech patterns is generated. The recognition process is to evaluate the probability of a TDC pattern belonging to a specific pattern class and to find the model which gives the highest probability. This method is applied to the recognition of Mandarin syllables. The experimental result shows that the proposed method is very promising for syllabic languages such as Mandarin.

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17

Leinonen, Lea, Jari Kangas, Kari Torkkola, and Anja Juvas. "Dysphonia Detected by Pattern Recognition of Spectral Composition." Journal of Speech, Language, and Hearing Research 35, no. 2 (1992): 287–95. http://dx.doi.org/10.1044/jshr.3502.287.

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The vowel [a:] in a test word, judged normal or dysphonic, was examined with the Self-Organizing Map, the artificial neural network algorithm of Kohonen. The algorithm produces two-dimensional representations (maps) of speech. Input to the acoustic maps consisted of 15-component spectral vectors calculated at 9.83-msec intervals from short-time power spectra. The male and female maps were first calculated from the speech of healthy subjects and then the [a:] samples (15 successive spectral vectors) were examined on the maps. The dysphonic voices deviated from the norm both in the composition of the short-time power spectra (characterized by the dislocation of the trajectory pattern on the map) and in the stability of the spectrum during the performance (characterized by the pattern of the trajectory on the map). Rough voices were distinguished from breathy ones by their patterns on the map. With the limited speech material, an index for the degree of pathology could not be determined. A self-organized acoustic map provides an on-line visual representation of voice and speech in an easily understandable form. The method is thus suitable not only for diagnostic but also for educational and therapeutic purposes.

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18

Morito, Makoto. "Speech recognition for recognizing the category of an input speech pattern." Journal of the Acoustical Society of America 90, no. 1 (1991): 626. http://dx.doi.org/10.1121/1.402309.

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19

Bulbul, Halil Ibrahim. "Application of Bernstein and Pattern Recognition Methods for Speech Command Recognition." Journal of Applied Sciences 7, no. 20 (2007): 3063–68. http://dx.doi.org/10.3923/jas.2007.3063.3068.

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20

Wu Chou. "Discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition." Proceedings of the IEEE 88, no. 8 (2000): 1201–23. http://dx.doi.org/10.1109/5.880080.

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21

Lin, Chin-Teng, Hsi-Wen Nein, and Wei-Fen Lin. "SPEAKER ADAPTATION OF FUZZY-PERCEPTRON-BASED SPEECH RECOGNITION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 01 (1999): 1–30. http://dx.doi.org/10.1142/s0218488599000027.

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In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplane need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several examples are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.

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22

Salaja, Rosemary T., Ronan Flynn, and Michael Russell. "A Life-Based Classifier for Automatic Speech Recognition." Applied Mechanics and Materials 679 (October 2014): 189–93. http://dx.doi.org/10.4028/www.scientific.net/amm.679.189.

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Research in speech recognition has produced different approaches that have been used for the classification of speech utterances in the back-end of an automatic speech recognition (ASR) system. As speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. This paper proposes a new back-end classifier that is based on artificial life (ALife) and describes how the proposed classifier can be used in a speech recognition system.

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23

Leinonen, Lea, Tapip Hiltunen, Jari Kangas, Anja Juvas, and Heikki Rihkanen. "Detection of dysphonia by pattern recognition of speech spectra." Scandinavian Journal of Logopedics and Phoniatrics 18, no. 4 (1993): 159–67. http://dx.doi.org/10.3109/14015439309101362.

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24

Iizuka, Hiroshi, Makoto Morito, and Kozo Yamada. "Speaker independen telephone speech recognition and reference pattern generation." Journal of the Acoustical Society of Japan (E) 7, no. 3 (1986): 155–65. http://dx.doi.org/10.1250/ast.7.155.

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25

Collins, Leslie M., Gregory H. Wakefield, and Gail R. Feinman. "Temporal pattern discrimination and speech recognition under electrical stimulation." Journal of the Acoustical Society of America 96, no. 5 (1994): 2731–37. http://dx.doi.org/10.1121/1.411279.

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26

Mavandadi, Sam, Parham Aarabi, Keyvan Mohajer, and Maryam Modir Shanechi. "Post Recognition Speech Localization." International Journal of Speech Technology 8, no. 2 (2005): 173–80. http://dx.doi.org/10.1007/s10772-005-2168-4.

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27

Ding, Ing Jr, and Chih Ta Yen. "An EigenMLLR-Like Eigen-FLS Approach for Speech Pattern Recognition." Applied Mechanics and Materials 284-287 (January 2013): 3030–34. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3030.

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The Eigen-FLS using an eigenspace-based scheme to build up fuzzy logic system (FLS) fast for speech pattern recognition applications has been developed in the author’s previous works. However, speech pattern recognition by Eigen-FLS will still encounter a dissatisfactory recognition performance when the collected data for eigen value calculations of the FLS eigenspace, i.e. the eigen-decomposition process, is scarce. To regulate the influence of Eigen-FLS when data from a test speaker for eigen-decomposition is improper, this paper proposes an EigenMLLR-like Eigen-FLS approach. The developed EigenMLLR-like Eigen-FLS integrates the kernel idea of EigenMLLR speaker adaptation for properly adjusting the target speaker’s Eigen-FLS model in the eigenspace of FLS. EigenMLLR-like Eigen-FLS developed in this paper will be more robust than conventional Eigen-FLS in a speech pattern recognition application with an adverse condition of insufficient data from the speaker.

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28

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

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29

Wu Chou. "Correction to "discriminant-function-based minimum recognition error rate pattern-recognition approach to speech recognition"." Proceedings of the IEEE 88, no. 11 (2000): 1814. http://dx.doi.org/10.1109/jproc.2000.892717.

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30

Yu, Dahai, Ovidiu Ghita, Alistair Sutherland, and Paul F. Whelan. "A Novel Visual Speech Representation and HMM Classification for Visual Speech Recognition." IPSJ Transactions on Computer Vision and Applications 2 (2010): 25–38. http://dx.doi.org/10.2197/ipsjtcva.2.25.

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31

Jermsittiparsert, Kittisak, Abdurrahman Abdurrahman, Parinya Siriattakul, et al. "Pattern recognition and features selection for speech emotion recognition model using deep learning." International Journal of Speech Technology 23, no. 4 (2020): 799–806. http://dx.doi.org/10.1007/s10772-020-09690-2.

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32

BENGIO, YOSHUA. "A CONNECTIONIST APPROACH TO SPEECH RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 04 (1993): 647–67. http://dx.doi.org/10.1142/s0218001493000327.

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The task discussed in this paper is that of learning to map input sequences to output sequences. In particular, problems of phoneme recognition in continuous speech are considered, but most of the discussed techniques could be applied to other tasks, such as the recognition of sequences of handwritten characters. The systems considered in this paper are based on connectionist models, or artificial neural networks, sometimes combined with statistical techniques for recognition of sequences of patterns, stressing the integration of prior knowledge and learning. Different architectures for sequence and speech recognition are reviewed, including recurrent networks as well as hybrid systems involving hidden Markov models.

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33

Wang, Chenguang. "Transitory speech parts recognition." Speech Communication 7, no. 1 (1988): 98. http://dx.doi.org/10.1016/0167-6393(88)90026-x.

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34

TAKANO, Yusuke, and Kazuhiro KONDO. "Estimation of Speech Intelligibility Using Speech Recognition Systems." IEICE Transactions on Information and Systems E93-D, no. 12 (2010): 3368–76. http://dx.doi.org/10.1587/transinf.e93.d.3368.

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35

Banerjee, Shilpi, Laurel Olson, Karrie Recker, and Justyn Pisa. "Efficacy and effectiveness of a pattern-recognition algorithm." Hearing Journal 59, no. 10 (2006): 34. http://dx.doi.org/10.1097/01.hj.0000286006.98788.f6.

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36

Zhang, Ruiqiang, and Genichiro Kikui. "Integration of speech recognition and machine translation: Speech recognition word lattice translation." Speech Communication 48, no. 3-4 (2006): 321–34. http://dx.doi.org/10.1016/j.specom.2005.06.007.

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37

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

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38

Stephenson, T. A., M. M. Doss, and H. Bourlard. "Speech Recognition With Auxiliary Information." IEEE Transactions on Speech and Audio Processing 12, no. 3 (2004): 189–203. http://dx.doi.org/10.1109/tsa.2003.822631.

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39

Li, Dan. "Emotional Interactive Simulation System of English Speech Recognition in Virtual Context." Complexity 2020 (August 11, 2020): 1–11. http://dx.doi.org/10.1155/2020/9409630.

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With the development of virtual scenes, the degree of simulation and functions of virtual reality have been very complete, providing a new platform and perspective for teaching design. Firstly, the hidden Markov chain model is used to perform emotion recognition on English speech signals. English speech emotion recognition and speech semantic recognition are essentially the same. Hidden Markov style has been widely used in English speech semantic recognition. The experiments of feature extraction and pattern recognition of speech samples prove that Hidden Markovian has higher recognition rate and better recognition effect in speech emotion recognition. Secondly, combining the human pronunciation model and the hearing model, by analyzing the impact of the glottis feature on the human ear hearing-model feature, the research application of the English speech recognition emotion interactive simulation system uses the glottis feature to compensate the human ear, hearing feature is proposed by compensated English speech recognition, and emotion interaction simulation system is used in the English speech emotion experiment, which has obtained a high recognition rate and showed excellent performance.

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40

Thoppil, MinuGeorge, CSanthosh Kumar, Anand Kumar, and John Amose. "Speech signal analysis and pattern recognition in diagnosis of dysarthria." Annals of Indian Academy of Neurology 20, no. 4 (2017): 352. http://dx.doi.org/10.4103/aian.aian_130_17.

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41

Yadav, Munshi, and Afshar Alam. "Reduction of Computation Time in Pattern Matching for Speech Recognition." International Journal of Computer Applications 90, no. 18 (2014): 35–37. http://dx.doi.org/10.5120/15823-4695.

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42

Iso, Ken-ichi. "Speech recognition by neural network adapted to reference pattern learning." Laboratory Automation & Information Management 33, no. 2 (1997): 143. http://dx.doi.org/10.1016/s1381-141x(97)80016-0.

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43

Kitazume, Yoshiaki. "Method and apparatus for registering standard pattern for speech recognition." Journal of the Acoustical Society of America 88, no. 1 (1990): 593. http://dx.doi.org/10.1121/1.399876.

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44

Nakadai, Yoshio. "Method and apparatus for word speech recognition by pattern matching." Journal of the Acoustical Society of America 104, no. 5 (1998): 2558. http://dx.doi.org/10.1121/1.423809.

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45

Larkey, Leah S. "Speech recognition apparatus and method having dynamic reference pattern adaptation." Journal of the Acoustical Society of America 94, no. 6 (1993): 3539. http://dx.doi.org/10.1121/1.407137.

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46

Kitazume, Y., E. Ohira, and Takeyuki Endo. "LSI implementation of a pattern matching algorithm for speech recognition." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 1 (1985): 1–4. http://dx.doi.org/10.1109/tassp.1985.1164510.

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47

Syahrul, Syahrul, and Mochamad Fajar Wicaksono. "KOMUNIKATOR TUNARUNGU DAN TUNANETRA." CCIT Journal 6, no. 1 (2012): 92–101. http://dx.doi.org/10.33050/ccit.v6i1.675.

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This paper describes the design and implementation of the deaf and blind communicator with the aim that they can communicate by using a Braille Codes per character and speech recognition using a computer. Communicator device is designed using AT89C51 microcontroller to change the characters that is sent by the computer into Braille code. The signal transmitted from the computer by hearing impairment through RS-232 serial interface with the help of driver IC ULN2803 to drive the solenoid. The end of the solenoid form a pattern of Braille code. Speech pattern recognition used are dictation mode with discrete speech method, whole word, large vocabulary and speaker dependent is designed with SAPI 5.1, Microsoft Speech Engine SDK 5.1 and Delphi software 6.0 to create an application program. On testing who performed indicate that the all the characters that is sent from keyboard can be converted become Braille Character who represented through the solenoid. While the recognition of sound patterns of the microphone most of the well can be translated into the characters displayed on a computer monitor. The success rate in speech recognition can be influenced by several factors such as differences in the sound at the time of training and at the time of dictation, noise from the environment and the quality of the microphone being used.

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48

de la Torre, A., A. M. Peinado, J. C. Segura, J. L. Perez-Cordoba, M. C. Benitez, and A. J. Rubio. "Histogram equalization of speech representation for robust speech recognition." IEEE Transactions on Speech and Audio Processing 13, no. 3 (2005): 355–66. http://dx.doi.org/10.1109/tsa.2005.845805.

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49

Finn, Kathleen E., and Allen A. Montgomery. "Automatic optically-based recognition of speech." Pattern Recognition Letters 8, no. 3 (1988): 159–64. http://dx.doi.org/10.1016/0167-8655(88)90094-3.

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50

Cardin, Regis, Renato De Mori, and Jean Rouat. "Property extraction for automatic speech recognition." Pattern Recognition Letters 10, no. 2 (1989): 127–37. http://dx.doi.org/10.1016/0167-8655(89)90077-9.

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