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Journal articles on the topic 'Computer recognition of speech'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Moese, Gerarld. "Computer system for speech recognition." Journal of the Acoustical Society of America 99, no. 2 (1996): 646. http://dx.doi.org/10.1121/1.414609.

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2

Kushida, Akihiro, and Tetsuo Kosaka. "Speech recognition system, speech recognition server, speech recognition client, their control method, and computer readable memory." Journal of the Acoustical Society of America 121, no. 3 (2007): 1290. http://dx.doi.org/10.1121/1.2720066.

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3

Bordeaux, Theodore A. "Real time computer speech recognition system." Journal of the Acoustical Society of America 89, no. 3 (1991): 1489. http://dx.doi.org/10.1121/1.400618.

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4

Vidal, E., F. Casacuberta, L. Rodriguez, J. Civera, and C. D. M. Hinarejos. "Computer-assisted translation using speech recognition." IEEE Transactions on Audio, Speech and Language Processing 14, no. 3 (2006): 941–51. http://dx.doi.org/10.1109/tsa.2005.857788.

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5

Schuller, Björn W. "Speech emotion recognition." Communications of the ACM 61, no. 5 (2018): 90–99. http://dx.doi.org/10.1145/3129340.

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6

Rebman, Carl M., Milam W. Aiken, and Casey G. Cegielski. "Speech recognition in the human–computer interface." Information & Management 40, no. 6 (2003): 509–19. http://dx.doi.org/10.1016/s0378-7206(02)00067-8.

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7

Lung, Vu Duc, Phan Dinh Duy, Nguyen Vo An Phu, Nguyen Hoang Long, and Truong Nguyen Vu. "Speech Recognition in Human-Computer Interactive Control." Journal of Automation and Control Engineering 1, no. 3 (2013): 222–26. http://dx.doi.org/10.12720/joace.1.3.222-226.

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8

Rengger, Ralph E., and David R. Manning. "Input device for computer speech recognition system." Journal of the Acoustical Society of America 83, no. 1 (1988): 405. http://dx.doi.org/10.1121/1.396213.

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9

A, Prof Swethashree. "Speech Emotion Recognition." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (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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10

Sudoh, Katsuhito, Hajime Tsukada, and Hideki Isozaki. "Named Entity Recognition from Speech Using Discriminative Models and Speech Recognition Confidence." Journal of Information Processing 17 (2009): 72–81. http://dx.doi.org/10.2197/ipsjjip.17.72.

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11

Grant, P. M. "Speech recognition techniques." Electronics & Communications Engineering Journal 3, no. 1 (1991): 37. http://dx.doi.org/10.1049/ecej:19910007.

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12

Alewine, N., H. Ruback, and S. Deligne. "Pervasive speech recognition." IEEE Pervasive Computing 3, no. 4 (2004): 78–81. http://dx.doi.org/10.1109/mprv.2004.16.

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13

Fried, Louis. "AUTOMATIC SPEECH RECOGNITION." Information Systems Management 13, no. 1 (1996): 29–37. http://dx.doi.org/10.1080/10580539608906969.

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14

Kabari, Ledisi Giok, and Marcus B. Chigoziri. "Speech Recognition Using MATLAB and Cross-Correlation Technique." European Journal of Engineering Research and Science 4, no. 8 (2019): 1–3. http://dx.doi.org/10.24018/ejers.2019.4.8.1437.

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Speech is a prominent communication method among humans, whereas the communication between human and computers were based on text user interface and graphic user interface. Speech recognition is used in almost every security project where you need to speak and tell your password to computer and is also used for automation. This paper demonstrates a model that enhances technological advancement where humans and computers interact via voice user interface. In developing the model, cross correlation was implemented in MATLAB to compare two or more signals and detect the most accurate one of the all. We are actually used cross correlation to find similarity between our recorded Signal files and the testing signal. Thus we were able to develop a model where machines can differentiate between commands and act upon them.

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15

Kotyk, Vladyslav, and Oksana Lashko. "Software Implementation of Gesture Recognition Algorithm Using Computer Vision." Advances in Cyber-Physical Systems 6, no. 1 (2021): 21–26. http://dx.doi.org/10.23939/acps2021.01.021.

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This paper examines the main methods and principles of image formation, display of the sign language recognition algorithm using computer vision to improve communication between people with hearing and speech impairments. This algorithm allows to effectively recognize gestures and display information in the form of labels. A system that includes the main modules for implementing this algorithm has been designed. The modules include the implementation of perception, transformation and image processing, the creation of a neural network using artificial intelligence tools to train a model for predicting input gesture labels. The aim of this work is to create a full-fledged program for implementing a real-time gesture recognition algorithm using computer vision and machine learning.

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16

Younis, Lujain, and Yusra Faisal. "Speaker Dependent Speech Recognition in Computer Game Control." International Journal of Computer Applications 158, no. 4 (2017): 32–38. http://dx.doi.org/10.5120/ijca2017912781.

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17

Wilson, Bronagh Blaney, John. "Acoustic variability in dysarthria and computer speech recognition." Clinical Linguistics & Phonetics 14, no. 4 (2000): 307–27. http://dx.doi.org/10.1080/02699200050024001.

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18

Ramakrishnan, S., and Ibrahiem M. M. El Emary. "Speech emotion recognition approaches in human computer interaction." Telecommunication Systems 52, no. 3 (2011): 1467–78. http://dx.doi.org/10.1007/s11235-011-9624-z.

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19

Yamada, Yusuke, Yuya Chiba, Takashi Nose, and Akinori Ito. "Effect of Training Data Selection for Speech Recognition of Emotional Speech." International Journal of Machine Learning and Computing 11, no. 5 (2021): 362–66. http://dx.doi.org/10.18178/ijmlc.2021.11.5.1062.

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20

Prager, R. W., T. D. Harrison, and F. Fallside. "Boltzmann machines for speech recognition." Computer Speech & Language 1, no. 1 (1986): 3–27. http://dx.doi.org/10.1016/s0885-2308(86)80008-0.

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21

Cerisara, Christophe, and Dominique Fohr. "Multi-band automatic speech recognition." Computer Speech & Language 15, no. 2 (2001): 151–74. http://dx.doi.org/10.1006/csla.2001.0163.

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22

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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23

Juang, B. H. "Speech recognition in adverse environments." Computer Speech & Language 5, no. 3 (1991): 275–94. http://dx.doi.org/10.1016/0885-2308(91)90011-e.

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24

Kramer, Tom, and Robert Kennedy. "Speech-Recognition Technology for Computers." Academic Psychiatry 23, no. 1 (1999): 48–50. http://dx.doi.org/10.1007/bf03340037.

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25

Liu, Jia Rong. "A Study on the Pros and Cons of Automatic Speech Translation - On the Speech to Speech Translation System Latest Achieved by Microsoft." Applied Mechanics and Materials 727-728 (January 2015): 982–86. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.982.

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Focusing on the speech recognition function of Automatic Speech Translation System, applying the figures and data collected from tests, and testing the deduced formula, the thesis mainly compares the computer-aided speech recognition with the pure manual one. As shown by the results, during the whole process of speech recognition, the computer-aided one consumes less time than the pure manual one does, and this time remains to be 2 seconds. It can be said that the technological feature, “little time-consuming”, ensures the system’s superiority of spreading information in unit time.

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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

kuchibhotla, Swarna. "Speech Emotion Recognition: A Survey." International Journal of Multimedia and Ubiquitous Engineering 14, no. 2 (2019): 15–22. http://dx.doi.org/10.21742/ijmue.2019.14.2.03.

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28

Shneiderman, Ben. "The limits of speech recognition." Communications of the ACM 43, no. 9 (2000): 63–65. http://dx.doi.org/10.1145/348941.348990.

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29

Buchsbaum, Adam L., and Raffaele Giancarlo. "Algorithmic aspects in speech recognition." ACM Journal of Experimental Algorithmics 2 (January 1997): 1. http://dx.doi.org/10.1145/264216.264219.

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30

Sherkawi, Lina, Nada Ghneim, and Oumayma Al Dakkak. "Arabic Speech Act Recognition Techniques." ACM Transactions on Asian and Low-Resource Language Information Processing 17, no. 3 (2018): 1–12. http://dx.doi.org/10.1145/3170576.

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31

Padmanabhan, M., and M. Picheny. "Large-vocabulary speech recognition algorithms." Computer 35, no. 3 (2002): 42–50. http://dx.doi.org/10.1109/2.993770.

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32

Padmanabhan, M., and M. Picheny. "Large-vocabulary speech recognition algorithms." Computer 35, no. 4 (2002): 42–50. http://dx.doi.org/10.1109/mc.2002.993770.

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33

Ferrier, Linda, Howard Shane, Holly Ballard, Tyler Carpenter, and Anne Benoit. "Dysarthric speakers' intelligibility and speech characteristics in relation to computer speech recognition." Augmentative and Alternative Communication 11, no. 3 (1995): 165–75. http://dx.doi.org/10.1080/07434619512331277289.

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34

Noyes, Jan M., Chris Baber, and Andrew P. Leggatt. "Automatic Speech Recognition, Noise and Workload." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (2000): 762–65. http://dx.doi.org/10.1177/154193120004402269.

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Despite the increasing use of technology in the developed world, most computer communications still take place via a QWERTY keyboard and a mouse. The use of Automatic Speech Recognition (ASR) whereby individuals can ‘talk’ to their computers has yet to be realised to any great extent. This is despite the benefits relating to greater efficiency, use in adverse environments and in the ‘hands-eyes busy’ situation. There are now affordable ASR products in the marketplace, and many people are able to buy these products and try ASR for themselves. However, anecdotal reports suggest that these same people will use ASR for a few days or weeks and then revert to conventional interaction techniques; only a hardy few appear to persist long enough to reap the benefits. Thus, it is our contention that ASR is a commercially viable technology but that it still requires further development to make a significant contribution to usability. Admittedly, there are some very successful applications that have used ASR for a number of decades, but these are often characterised by relatively small vocabularies, dedicated users and non-threatening situations; typical applications are in offices (Noyes & Frankish, 1989) or for disabled users (Noyes & Frankish, 1992). Given that Armoured Fighting Vehicles (AFVs) could employ ASR with limited vocabulary and dedicated users, the use of ASR in this application is considered here. The principle difference between ASR for AFV and previous applications is the environmental conditions in which the technology will be used.

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35

Hiner, L. E. "Speed of Speech Recognition versus Keyboarding as Computer Input Devices for the Severely Disabled." Journal of Educational Technology Systems 16, no. 3 (1988): 283–93. http://dx.doi.org/10.2190/rbc7-f6hb-mxnl-f72f.

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Speech recognition systems promise to facilitate access to computer for users with disability. This study examined the usefulness of the Texas Instruments Speech Recognition System in completing word processing tasks under the experimental conditions of 1) keyboard input only, 2) speech recognition only, and 3) a combination of keyboard input and speech recognition. Five subjects with some degree of upper-body disability were tested; the results indicate that performance was 1) greatest under the keyboard only condition, 2) lowest under the speech only condition, and 3) somewhat lower under the combined condition than under the keyboard only condition. Based on the findings, suggestions for further research were made.

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36

Duncan, G. "Speech Recognition by Machine." Electronics & Communications Engineering Journal 1, no. 2 (1989): 91. http://dx.doi.org/10.1049/ecej:19890017.

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37

Renals, S. "Review: Using Speech Recognition." Computer Bulletin 38, no. 6 (1996): 27. http://dx.doi.org/10.1093/combul/38.6.27.

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38

Lee, Yun Kyung, and Jeon Gue Park. "Multimodal Unsupervised Speech Translation for Recognizing and Evaluating Second Language Speech." Applied Sciences 11, no. 6 (2021): 2642. http://dx.doi.org/10.3390/app11062642.

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This paper addresses an automatic proficiency evaluation and speech recognition for second language (L2) speech. The proposed method recognizes the speech uttered by the L2 speaker, measures a variety of fluency scores, and evaluates the proficiency of the speaker’s spoken English. Stress and rhythm scores are one of the important factors used to evaluate fluency in spoken English and are computed by comparing the stress patterns and the rhythm distributions to those of native speakers. In order to compute the stress and rhythm scores even when the phonemic sequence of the L2 speaker’s English sentence is different from the native speaker’s one, we align the phonemic sequences based on a dynamic time-warping approach. We also improve the performance of the speech recognition system for non-native speakers and compute fluency features more accurately by augmenting the non-native training dataset and training an acoustic model with the augmented dataset. In this work, we augment the non-native speech by converting some speech signal characteristics (style) while preserving its linguistic information. The proposed variational autoencoder (VAE)-based speech conversion network trains the conversion model by decomposing the spectral features of the speech into a speaker-invariant content factor and a speaker-specific style factor to estimate diverse and robust speech styles. Experimental results show that the proposed method effectively measures the fluency scores and generates diverse output signals. Also, in the proficiency evaluation and speech recognition tests, the proposed method improves the proficiency score performance and speech recognition accuracy for all proficiency areas compared to a method employing conventional acoustic models.

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39

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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40

Du, Haifan, and Haiwen Duan. "English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints." Complexity 2021 (May 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/8482379.

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This paper combines domestic and international research results to analyze and study the difference between the attribute features of English phrase speech and noise to enhance the short-time energy, which is used to improve the threshold judgment sensitivity; noise addition to the discrepancy data set is used to enhance the recognition robustness. The backpropagation algorithm is improved to constrain the range of weight variation, avoid oscillation phenomenon, and shorten the training time. In the real English phrase sound recognition system, there are problems such as massive training data and low training efficiency caused by the super large-scale model parameters of the convolutional neural network. To address these problems, the NWBP algorithm is based on the oscillation phenomenon that tends to occur when searching for the minimum error value in the late training period of the network parameters, using the K-MEANS algorithm to obtain the seed nodes that approach the minimal error value, and using the boundary value rule to reduce the range of weight change to reduce the oscillation phenomenon so that the network error converges as soon as possible and improve the training efficiency. Through simulation experiments, the NWBP algorithm improves the degree of fitting and convergence speed in the training of complex convolutional neural networks compared with other algorithms, reduces the redundant computation, and shortens the training time to a certain extent, and the algorithm has the advantage of accelerating the convergence of the network compared with simple networks. The word tree constraint and its efficient storage structure are introduced, which improves the storage efficiency of the word tree constraint and the retrieval efficiency in the English phrase recognition search.

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41

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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42

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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43

Mamyrbayev, Orken Zh, Keylan Alimhan, Beibut Amirgaliyev, Bagashar Zhumazhanov, Dinara Mussayeva, and Farida Gusmanova. "Multimodal systems for speech recognition." International Journal of Mobile Communications 18, no. 3 (2020): 314. http://dx.doi.org/10.1504/ijmc.2020.107097.

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44

SHAIKH, AYAZ A., DINESH K. KUMAR, and JAYAVARDHANA GUBBI. "VISUAL SPEECH RECOGNITION USING OPTICAL FLOW AND SUPPORT VECTOR MACHINES." International Journal of Computational Intelligence and Applications 10, no. 02 (2011): 167–87. http://dx.doi.org/10.1142/s1469026811003045.

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A lip-reading technique that identifies visemes from visual data only and without evaluating the corresponding acoustic signals is presented. The technique is based on vertical components of the optical flow (OF) analysis and these are classified using support vector machines (SVM). The OF is decomposed into multiple non-overlapping fixed scale blocks and statistical features of each block are computed for successive video frames of an utterance. This technique performs automatic temporal segmentation (i.e., determining the start and the end of an utterance) of the utterances, achieved by pair-wise pixel comparison method, which evaluates the differences in intensity of corresponding pixels in two successive frames. The experiments were conducted on a database of 14 visemes taken from seven subjects and the accuracy tested using five and ten fold cross validation for binary and multiclass SVM respectively to determine the impact of subject variations. Unlike other systems in the literature, the results indicate that the proposed method is more robust to inter-subject variations with high sensitivity and specificity for 12 out of 14 visemes. Potential applications of such a system include human computer interface (HCI) for mobility-impaired users, lip reading mobile phones, in-vehicle systems, and improvement of speech based computer control in noisy environment.

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45

Jeong, Hae-Duck, Sang-Kug Ye, Jiyoung Lim, Ilsun You, and Wooseok Hyun. "A computer remote control system based on speech recognition technologies of mobile devices and wireless communication technologies." Computer Science and Information Systems 11, no. 3 (2014): 1001–16. http://dx.doi.org/10.2298/csis130915061j.

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This paper presents a computer remote control system using speech recognition technologies of mobile devices and wireless communication technologies for the blind and physically disabled population as assistive technology. These people experience difficulty and inconvenience using computers through a keyboard and/or mouse. The purpose of this system is to provide a way that the blind and physically disabled population can easily control many functions of a computer via speech. The configuration of the system consists of a mobile device such as a smartphone, a PC server, and a Google server that are connected to each other. Users can command a mobile device to do something via speech; such as writing emails, checking the weather forecast, or managing a schedule. These commands are then immediately executed. The proposed system also provides blind people with a function via TTS(Text To Speech) of the Google server if they want to receive contents of a document stored in a computer.

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46

Gerhana, Y. A., A. R. Atmadja, D. S. Maylawati, et al. "Computer speech recognition to text for recite Holy Quran." IOP Conference Series: Materials Science and Engineering 434 (December 4, 2018): 012044. http://dx.doi.org/10.1088/1757-899x/434/1/012044.

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47

Deller, J. R., C. G. Venkatesh, D. Hsu, L. J. Ferrier, and M. B. Cozzens. "Recent progress in computer recognition of cerebral palsy speech." Journal of the Acoustical Society of America 81, S1 (1987): S56. http://dx.doi.org/10.1121/1.2024298.

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48

Silsbee, P. L., and A. C. Bovik. "Computer lipreading for improved accuracy in automatic speech recognition." IEEE Transactions on Speech and Audio Processing 4, no. 5 (1996): 337–51. http://dx.doi.org/10.1109/89.536928.

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49

Yamada, Masayuki. "Speech recognition method and apparatus and computer-readable memory." Journal of the Acoustical Society of America 113, no. 3 (2003): 1201. http://dx.doi.org/10.1121/1.1566358.

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50

Sonoura, Takafumi, and Kaoru Suzuki. "Interactive robot, speech recognition method and computer program product." Journal of the Acoustical Society of America 128, no. 3 (2010): 1568. http://dx.doi.org/10.1121/1.3490385.

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