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

Author: Grafiati

Published: 17 September 2021

Last updated: 1 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

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.

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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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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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11

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

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12

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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13

Bulbul, Halil Ibrahim. "Application of Bernstein and Pattern Recognition Methods for Speech Command Recognition." Journal of Applied Sciences 7, no. 20 (October 1, 2007): 3063–68. http://dx.doi.org/10.3923/jas.2007.3063.3068.

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14

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

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15

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

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16

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

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17

AGUILAR, G. "Alaryngeal Speech Enhancement Using Pattern Recognition Techniques." IEICE Transactions on Information and Systems E88-D, no. 7 (July 1, 2005): 1618–22. http://dx.doi.org/10.1093/ietisy/e88-d.7.1618.

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18

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

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19

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 (February 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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20

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

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21

A, Prof Swethashree. "Speech Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2637–40. http://dx.doi.org/10.22214/ijraset.2021.37375.

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Abstract: Speech Emotion Recognition, abbreviated as SER, the act of trying to identify a person's feelings and relationships. Affected situations from speech. This is because the truth often reflects the basic feelings of tone and tone of voice. Emotional awareness is a fast-growing field of research in recent years. Unlike humans, machines do not have the power to comprehend and express emotions. But human communication with the computer can be improved by using automatic sensory recognition, accordingly reducing the need for human intervention. In this project, basic emotions such as peace, happiness, fear, disgust, etc. are analyzed signs of emotional expression. We use machine learning techniques such as Multilayer perceptron Classifier (MLP Classifier) which is used to separate information provided by groups to be divided equally. Coefficients of Mel-frequency cepstrum (MFCC), chroma and mel features are extracted from speech signals and used to train MLP differentiation. By accomplishing this purpose, we use python libraries such as Librosa, sklearn, pyaudio, numpy and audio file to analyze speech patterns and see the feeling. Keywords: Speech emotion recognition, mel cepstral coefficient, neural artificial network, multilayer perceptrons, mlp classifier, python.

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22

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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23

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 (April 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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24

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 (February 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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25

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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26

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

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27

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

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28

Jermsittiparsert, Kittisak, Abdurrahman Abdurrahman, Parinya Siriattakul, Ludmila A. Sundeeva, Wahidah Hashim, Robbi Rahim, and Andino Maseleno. "Pattern recognition and features selection for speech emotion recognition model using deep learning." International Journal of Speech Technology 23, no. 4 (September 8, 2020): 799–806. http://dx.doi.org/10.1007/s10772-020-09690-2.

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29

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

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30

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

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31

BENGIO, YOSHUA. "A CONNECTIONIST APPROACH TO SPEECH RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 04 (August 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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32

LEE, K. S. "Robust Recognition of Fast Speech." IEICE Transactions on Information and Systems E89-D, no. 8 (August 1, 2006): 2456–59. http://dx.doi.org/10.1093/ietisy/e89-d.8.2456.

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33

Schmitt, Alexander, Dmitry Zaykovskiy, and Wolfgang Minker. "Speech recognition for mobile devices." International Journal of Speech Technology 11, no. 2 (June 2008): 63–72. http://dx.doi.org/10.1007/s10772-009-9036-6.

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34

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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35

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 (January 1993): 159–67. http://dx.doi.org/10.3109/14015439309101362.

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36

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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37

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 (November 1994): 2731–37. http://dx.doi.org/10.1121/1.411279.

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38

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

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39

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

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40

Ntalampiras, Stavros. "Speech emotion recognition via learning analogies." Pattern Recognition Letters 144 (April 2021): 21–26. http://dx.doi.org/10.1016/j.patrec.2021.01.018.

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41

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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42

Lebedev, V. G., and N. G. Zagoruiko. "Auditory perception and speech recognition." Speech Communication 4, no. 1-3 (August 1985): 97–103. http://dx.doi.org/10.1016/0167-6393(85)90038-x.

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43

Koo, J. M., and C. K. Un. "A recognition time reduction algorithm for large-vocabulary speech recognition." Speech Communication 11, no. 1 (March 1992): 45–50. http://dx.doi.org/10.1016/0167-6393(92)90062-c.

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44

Sorokin, V. N., and A. S. Leonov. "Multisource Speech Analysis for Speaker Recognition." Pattern Recognition and Image Analysis 29, no. 1 (January 2019): 181–93. http://dx.doi.org/10.1134/s1054661818040260.

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45

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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46

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

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47

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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48

Chulhee Lee, Donghoon Hyun, Euisun Choi, Jinwook Go, and Chungyong Lee. "Optimizing feature extraction for speech recognition." IEEE Transactions on Speech and Audio Processing 11, no. 1 (January 2003): 80–87. http://dx.doi.org/10.1109/tsa.2002.805644.

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49

McAuley, J., Ji Ming, D. Stewart, and P. Hanna. "Subband correlation and robust speech recognition." IEEE Transactions on Speech and Audio Processing 13, no. 5 (September 2005): 956–64. http://dx.doi.org/10.1109/tsa.2005.851952.

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50

Gao, Mei Juan, and Zhi Xin Yang. "Research and Realization on the Voice Command Recognition System for Robot Control Based on ARM9." Applied Mechanics and Materials 44-47 (December 2010): 1422–26. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.1422.

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In this paper, based on the study of two speech recognition algorithms, two designs of speech recognition system are given to realize this isolated speech recognition mobile robot control system based on ARM9 processor. The speech recognition process includes pretreatment of speech signal, characteristic extrication, pattern matching and post-processing. Mel-Frequency cepstrum coefficients (MFCC) and linear prediction cepstrum coefficients (LPCC) are the two most common parameters. Through analysis and comparison the parameters, MFCC shows more noise immunity than LPCC, so MFCC is selected as the characteristic parameters. Both dynamic time warping (DTW) and hidden markov model (HMM) are commonly used algorithm. For the different characteristics of DTW and HMM recognition algorithm, two different programs were designed for mobile robot control system. The effect and speed of the two speech recognition system were analyzed and compared.

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