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Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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1

Beňo, Lukáš, Rudolf Pribiš, and Peter Drahoš. "Edge Container for Speech Recognition." Electronics 10, no. 19 (October 4, 2021): 2420. http://dx.doi.org/10.3390/electronics10192420.

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Анотація:
Containerization has been mainly used in pure software solutions, but it is gradually finding its way into the industrial systems. This paper introduces the edge container with artificial intelligence for speech recognition, which performs the voice control function of the actuator as a part of the Human Machine Interface (HMI). This work proposes a procedure for creating voice-controlled applications with modern hardware and software resources. The created architecture integrates well-known digital technologies such as containerization, cloud, edge computing and a commercial voice processing tool. This methodology and architecture enable the actual speech recognition and the voice control on the edge device in the local network, rather than in the cloud, like the majority of recent solutions. The Linux containers are designed to run without any additional configuration and setup by the end user. A simple adaptation of voice commands via configuration file may be considered as an additional contribution of the work. The architecture was verified by experiments with running containers on different devices, such as PC, Tinker Board 2, Raspberry Pi 3 and 4. The proposed solution and the practical experiment show how a voice-controlled system can be created, easily managed and distributed to many devices around the world in a few seconds. All this can be achieved by simple downloading and running two types of ready-made containers without any complex installations. The result of this work is a proven stable (network-independent) solution with data protection and low latency.
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2

Yadav, Apurv Singh. "Keyword Recognition Device Cloud Based." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 87–89. http://dx.doi.org/10.22214/ijraset.2021.37296.

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Анотація:
Over the past few decades speech recognition has been researched and developed tremendously. However in the past few years use of the Internet of things has been significantly increased and with it the essence of efficient speech recognition is beneficial more than ever. With the significant improvement in Machine Learning and Deep learning, speech recognition has become more efficient and applicable. This paper focuses on developing an efficient Speech recognition system using Deep Learning.
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3

SHINODA, Koichi. "Acoustic Model Adaptation for Speech Recognition." IEICE Transactions on Information and Systems E93.D, no. 9 (2010): 2348–62. http://dx.doi.org/10.1587/transinf.e93.d.2348.

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4

Takagi, Keizaburo, Hiroaki Hattori, and Takao Watanabe. "Rapid environment adaptation for speech recognition." Journal of the Acoustical Society of Japan (E) 16, no. 5 (1995): 273–81. http://dx.doi.org/10.1250/ast.16.273.

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5

Furui, Sadaoki. "Speaker adaptation techniques for speech recognition." Journal of the Institute of Television Engineers of Japan 43, no. 9 (1989): 929–34. http://dx.doi.org/10.3169/itej1978.43.929.

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6

Cox, Stephen. "Predictive speaker adaptation in speech recognition." Computer Speech & Language 9, no. 1 (January 1995): 1–17. http://dx.doi.org/10.1006/csla.1995.0001.

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7

Rajput, Nitendra, and Ashish Verma. "SPEAKER ADAPTATION OF VOCABULARY FOR SPEECH RECOGNITION." Journal of the Acoustical Society of America 132, no. 4 (2012): 2779. http://dx.doi.org/10.1121/1.4757837.

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8

Lee, Hyeopwoo, and Dongsuk Yook. "Feature adaptation for robust mobile speech recognition." IEEE Transactions on Consumer Electronics 58, no. 4 (November 2012): 1393–98. http://dx.doi.org/10.1109/tce.2012.6415011.

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9

Cung, H. M., and Y. Normandin. "Noise adaptation algorithms for robust speech recognition." Speech Communication 12, no. 3 (July 1993): 267–76. http://dx.doi.org/10.1016/0167-6393(93)90098-6.

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10

Yoshida, Kazunaga, and Takao Watanabe. "Speech recognition apparatus of speaker adaptation type." Journal of the Acoustical Society of America 95, no. 1 (January 1994): 592. http://dx.doi.org/10.1121/1.408288.

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11

Ma, Changxue, and Yuan-Jun Wei. "Speech recognition by dynamical noise model adaptation." Journal of the Acoustical Society of America 119, no. 6 (2006): 3526. http://dx.doi.org/10.1121/1.2212619.

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12

Komori, Yasuhiro, and Masayuki Yamada. "Environment adaptation for speech recognition in a speech communication system." Journal of the Acoustical Society of America 120, no. 5 (2006): 2416. http://dx.doi.org/10.1121/1.2395171.

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13

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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Анотація:
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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14

Chung, Yongjoo. "Noise Robust Speech Recognition Based on Noisy Speech Acoustic Model Adaptation." Phonetics and Speech Sciences 6, no. 2 (June 30, 2014): 29–34. http://dx.doi.org/10.13064/ksss.2014.6.2.029.

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15

Thatphithakkul, Nattanun, Boontee Kruatrachue, Chai Wutiwiwatchai, Sanparith Marukatat, and Vataya Boonpiam. "SIMULATED-DATA ADAPTATION BASED PIECEWISE LINEAR TRANSFORMATION FOR ROBUST SPEECH RECOGNITION." ASEAN Journal on Science and Technology for Development 24, no. 4 (November 16, 2017): 339–52. http://dx.doi.org/10.29037/ajstd.209.

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Анотація:
This paper proposes an efficient method of simulated-data adaptation for robust speech recognition. The method is applied to tree-structured piecewise linear transformation (PLT). The original PLT selects an acoustic model using tree-structured HMMs and the acoustic model is adapted by input speech in an unsupervised scheme. This adaptation can degrade the acoustic model if the input speech is incorrectly transcribed during the adaptation process. Moreover, adaptation may not be effective if only the input speech is used. Our proposed method increases the size of adaptation data by adding noise portions from the input speech to a set of prerecorded clean speech, of which correct transcriptions are known. We investigate various configurations of the proposed method. Evaluations are performed with both additive and real noisy speech. The experimental results show that the proposed system reaches higher recognition rate than MLLR, HMM-based model selection and PLT.
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16

Ban, Sung Min, and Hyung Soon Kim. "Instantaneous model adaptation method for reverberant speech recognition." Electronics Letters 51, no. 6 (March 2015): 528–30. http://dx.doi.org/10.1049/el.2014.4152.

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17

Shinoda, Koichi, Ken-Ichi Iso, and Takao Watanabe. "Speaker adaptation using spectral interpolation for speech recognition." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 77, no. 10 (1994): 1–11. http://dx.doi.org/10.1002/ecjc.4430771001.

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18

Takagi, Keizaburo. "Speech adaptation device suitable for speech recognition device and word spotting device." Journal of the Acoustical Society of America 107, no. 3 (2000): 1089. http://dx.doi.org/10.1121/1.428390.

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19

Chien, J. T., and H. C. Wang. "Adaptation of hidden Markov model for telephone speech recognition and speaker adaptation." IEE Proceedings - Vision, Image, and Signal Processing 144, no. 3 (1997): 129. http://dx.doi.org/10.1049/ip-vis:19971049.

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20

Deuerlein, Christian, Moritz Langer, Julian Seßner, Peter Heß, and Jörg Franke. "Human-robot-interaction using cloud-based speech recognition systems." Procedia CIRP 97 (2021): 130–35. http://dx.doi.org/10.1016/j.procir.2020.05.214.

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21

Schultz, Benjamin G., Venkata S. Aditya Tarigoppula, Gustavo Noffs, Sandra Rojas, Anneke van der Walt, David B. Grayden, and Adam P. Vogel. "Automatic speech recognition in neurodegenerative disease." International Journal of Speech Technology 24, no. 3 (May 4, 2021): 771–79. http://dx.doi.org/10.1007/s10772-021-09836-w.

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Анотація:
AbstractAutomatic speech recognition (ASR) could potentially improve communication by providing transcriptions of speech in real time. ASR is particularly useful for people with progressive disorders that lead to reduced speech intelligibility or difficulties performing motor tasks. ASR services are usually trained on healthy speech and may not be optimized for impaired speech, creating a barrier for accessing augmented assistance devices. We tested the performance of three state-of-the-art ASR platforms on two groups of people with neurodegenerative disease and healthy controls. We further examined individual differences that may explain errors in ASR services within groups, such as age and sex. Speakers were recorded while reading a standard text. Speech was elicited from individuals with multiple sclerosis, Friedreich’s ataxia, and healthy controls. Recordings were manually transcribed and compared to ASR transcriptions using Amazon Web Services, Google Cloud, and IBM Watson. Accuracy was measured as the proportion of words that were correctly classified. ASR accuracy was higher for controls than clinical groups, and higher for multiple sclerosis compared to Friedreich’s ataxia for all ASR services. Amazon Web Services and Google Cloud yielded higher accuracy than IBM Watson. ASR accuracy decreased with increased disease duration. Age and sex did not significantly affect ASR accuracy. ASR faces challenges for people with neuromuscular disorders. Until improvements are made in recognizing less intelligible speech, the true value of ASR for people requiring augmented assistance devices and alternative communication remains unrealized. We suggest potential methods to improve ASR for those with impaired speech.
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22

KOO, J. M., H. S. KIM, and C. K. UN. "A KOREAN LARGE VOCABULARY SPEECH RECOGNITION SYSTEM FOR AUTOMATIC TELEPHONE NUMBER QUERY SERVICE." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 01 (February 1994): 215–32. http://dx.doi.org/10.1142/s0218001494000103.

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In this paper, we introduce a Korean large vocabulary speech recognition system. This system recognizes sentence utterances with a vocabulary size of 1160 words, and is designed for an automatic telephone number query service. The system consists of four subsystems. The first is an acoustic processor recognizing words in an input sentence by a Hidden Markov Model (HMM) based speech recognition algorithm. The second subsystem is a linguistic processor which estimates input sentences from the results of the acoustic processor and determines the following words using syntactic information. The third is a time reduction processor reducing recognition time by limiting the number of candidate words to be computed by the acoustic processor. The time reduction processor uses linguistic information and acoustic information contained in the input sentence. The last subsystem is a speaker adaptation processor which quickly adapts parameters of the speech recognition system to new speakers. This subsystem uses VQ adaptation and HMM parameter adaptation based on spectral mapping. We also present our recent work on improving the performance of the large vocabulary speech recognition system. These works focused on the enhancement of the acoustic processor and the time reduction processor for speaker-independent speech recognition. A new approach for speaker adaptation is also described.
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23

Anggraini, Nenny, Angga Kurniawan, Luh Kesuma Wardhani, and Nashrul Hakiem. "Speech Recognition Application for the Speech Impaired using the Android-based Google Cloud Speech API." TELKOMNIKA (Telecommunication Computing Electronics and Control) 16, no. 6 (December 1, 2018): 2733. http://dx.doi.org/10.12928/telkomnika.v16i6.9638.

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24

Chung, Y. J. "Adaptation method using expectation-maximisation for noisy speech recognition." Electronics Letters 38, no. 13 (2002): 666. http://dx.doi.org/10.1049/el:20020428.

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25

Chung, Yong-Joo. "Speech Recognition based on Environment Adaptation using SNR Mapping." Journal of the Korea institute of electronic communication sciences 9, no. 5 (May 31, 2014): 543–48. http://dx.doi.org/10.13067/jkiecs.201.9.5.543.

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26

Deng, Jun, Zixing Zhang, Florian Eyben, and Bjorn Schuller. "Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion Recognition." IEEE Signal Processing Letters 21, no. 9 (September 2014): 1068–72. http://dx.doi.org/10.1109/lsp.2014.2324759.

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27

Deng, Jun, Xinzhou Xu, Zixing Zhang, Sascha Fruhholz, and Bjorn Schuller. "Universum Autoencoder-Based Domain Adaptation for Speech Emotion Recognition." IEEE Signal Processing Letters 24, no. 4 (April 2017): 500–504. http://dx.doi.org/10.1109/lsp.2017.2672753.

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28

Furui, Sadaoki, Daisuke Itoh, and Zhipeng Zhang. "Neural-network-based HMM adaptation for noisy speech recognition." Acoustical Science and Technology 24, no. 2 (2003): 69–75. http://dx.doi.org/10.1250/ast.24.69.

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29

Pirhosseinloo, Shadi, and Farshad Almas Ganj. "Discriminative speaker adaptation in Persian continuous speech recognition systems." Procedia - Social and Behavioral Sciences 32 (2012): 296–301. http://dx.doi.org/10.1016/j.sbspro.2012.01.043.

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30

KIM, T. Y. "Bayesian Confidence Scoring and Adaptation Techniques for Speech Recognition." IEICE Transactions on Communications E88-B, no. 4 (April 1, 2005): 1756–59. http://dx.doi.org/10.1093/ietcom/e88-b.4.1756.

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31

O’Shaughnessy, Douglas, Wayne Wang, William Zhu, Vincent Barreaud, T. Nagarajan, and R. Muralishankar. "Improving automatic speech recognition via better analysis and adaptation." Journal of the Acoustical Society of America 118, no. 3 (September 2005): 2027. http://dx.doi.org/10.1121/1.4785782.

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32

Zhu, Donglai, Satoshi Nakamura, Kuldip K. Paliwal, and Renhua Wang. "Maximum likelihood sub-band adaptation for robust speech recognition." Speech Communication 47, no. 3 (November 2005): 243–64. http://dx.doi.org/10.1016/j.specom.2005.02.006.

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33

Hao, Y. "Speech recognition using speaker adaptation by system parameter transformation." IEEE Transactions on Speech and Audio Processing 2, no. 1 (January 1994): 63–68. http://dx.doi.org/10.1109/89.260335.

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34

Cox, S. "Speaker adaptation in speech recognition using linear regression techniques." Electronics Letters 28, no. 22 (1992): 2093. http://dx.doi.org/10.1049/el:19921342.

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35

Shinoda, Koichi. "Speaker adaptation techniques for speech recognition using probabilistic models." Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 88, no. 12 (2005): 25–42. http://dx.doi.org/10.1002/ecjc.20207.

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36

Matsui, Tomoko, and Sadaoki Furui. "N-Best-based unsupervised speaker adaptation for speech recognition." Computer Speech & Language 12, no. 1 (January 1998): 41–50. http://dx.doi.org/10.1006/csla.1997.0036.

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37

Huang, Zhengwei, Wentao Xue, Qirong Mao, and Yongzhao Zhan. "Unsupervised domain adaptation for speech emotion recognition using PCANet." Multimedia Tools and Applications 76, no. 5 (February 22, 2016): 6785–99. http://dx.doi.org/10.1007/s11042-016-3354-x.

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38

MA, Han, Qiaoling ZHANG, Roubing TANG, Lu ZHANG, and Yubo JIA. "Robust Speech Recognition Using Teacher-Student Learning Domain Adaptation." IEICE Transactions on Information and Systems E105.D, no. 12 (December 1, 2022): 2112–18. http://dx.doi.org/10.1587/transinf.2022edp7043.

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39

Wongpatikaseree, Konlakorn, Sattaya Singkul, Narit Hnoohom, and Sumeth Yuenyong. "Real-Time End-to-End Speech Emotion Recognition with Cross-Domain Adaptation." Big Data and Cognitive Computing 6, no. 3 (July 15, 2022): 79. http://dx.doi.org/10.3390/bdcc6030079.

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Анотація:
Language resources are the main factor in speech-emotion-recognition (SER)-based deep learning models. Thai is a low-resource language that has a smaller data size than high-resource languages such as German. This paper describes the framework of using a pretrained-model-based front-end and back-end network to adapt feature spaces from the speech recognition domain to the speech emotion classification domain. It consists of two parts: a speech recognition front-end network and a speech emotion recognition back-end network. For speech recognition, Wav2Vec2 is the state-of-the-art for high-resource languages, while XLSR is used for low-resource languages. Wav2Vec2 and XLSR have proposed generalized end-to-end learning for speech understanding based on the speech recognition domain as feature space representations from feature encoding. This is one reason why our front-end network was selected as Wav2Vec2 and XLSR for the pretrained model. The pre-trained Wav2Vec2 and XLSR are used for front-end networks and fine-tuned for specific languages using the Common Voice 7.0 dataset. Then, feature vectors of the front-end network are input for back-end networks; this includes convolution time reduction (CTR) and linear mean encoding transformation (LMET). Experiments using two different datasets show that our proposed framework can outperform the baselines in terms of unweighted and weighted accuracies.
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40

Liu, Xue Yan, and Bao Ling Yuan. "Research on Speech Recognition System with Speaker Identification Based on the Cloud Server." Advanced Materials Research 1022 (August 2014): 219–22. http://dx.doi.org/10.4028/www.scientific.net/amr.1022.219.

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Анотація:
The speech recognition system is not real-time, a speak identification method based on the cloud server is proposed to solve this problem. Firstly, the MFCC frequency cepstrum coefficient and the first order differential coefficient are extracted from the speech feature vector sequence to form 32 dimensional. And then the 32 dimensional speech feature vector is sent to the cloud server, the training speaker model and identification are done in the cloud server. Finally, the identification result is sent to the client.
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41

Bhardwaj, Vivek, Vinay Kukreja, and Amitoj Singh. "Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System." Revue d'Intelligence Artificielle 35, no. 3 (June 30, 2021): 235–42. http://dx.doi.org/10.18280/ria.350307.

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Анотація:
Most of the automatic speech recognition (ASR) systems are trained using adult speech due to the less availability of the children's speech dataset. The speech recognition rate of such systems is very less when tested using the children's speech, due to the presence of the inter-speaker acoustic variabilities between the adults and children's speech. These inter-speaker acoustic variabilities are mainly because of the higher pitch and lower speaking rate of the children. Thus, the main objective of the research work is to increase the speech recognition rate of the Punjabi-ASR system by reducing these inter-speaker acoustic variabilities with the help of prosody modification and speaker adaptive training. The pitch period and duration (speaking rate) of the speech signal can be altered with prosody modification without influencing the naturalness, message of the signal and helps to overcome the acoustic variations present in the adult's and children's speech. The developed Punjabi-ASR system is trained with the help of adult speech and prosody-modified adult speech. This prosody modified speech overcomes the massive need for children's speech for training the ASR system and improves the recognition rate. Results show that prosody modification and speaker adaptive training helps to minimize the word error rate (WER) of the Punjabi-ASR system to 8.79% when tested using children's speech.
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42

Stoyanchev, Svetlana, and Amanda J. Stent. "Concept Type Prediction and Responsive Adaptation in a Dialogue System." Dialogue & Discourse 3, no. 1 (February 10, 2012): 1–31. http://dx.doi.org/10.5087/dad.2012.101.

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Анотація:
Responsive adaptation in spoken dialog systems involves a change in dialog system behavior in response to a user or a dialog situation. In this paper we address responsive adaptation in the automatic speech recognition (ASR) module of a spoken dialog system. We hypothesize that information about the content of a user utterance may help improve speech recognition for the utterance. We use a two-step process to test this hypothesis: first, we automatically predict the task-relevant concept types likely to be present in a user utterance using features from the dialog context and from the output of first-pass ASR of the utterance; and then, we adapt the ASR's language model to the predicted content of the user's utterance and run a second pass of ASR. We show that: (1) it is possible to achieve high accuracy in determining presence or absence of particular concept types in a post-confirmation utterance; and (2) 2-pass speech recognition with concept type classification and language model adaptation can lead to improved speech recognition performance for post-confirmation utterances.
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43

Yoo, Hyun-Jae, Myung-Wha Kim, Sang-Kil Park, and Kwang-Yong Kim. "Comparative Analysis of Korean Continuous Speech Recognition Accuracy by Application Field of Cloud-Based Speech Recognition Open API." Journal of Korean Institute of Communications and Information Sciences 45, no. 10 (October 31, 2020): 1793–803. http://dx.doi.org/10.7840/kics.2020.45.10.1793.

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44

Hossain, M. Shamim, and Ghulam Muhammad. "Cloud-Assisted Speech and Face Recognition Framework for Health Monitoring." Mobile Networks and Applications 20, no. 3 (February 22, 2015): 391–99. http://dx.doi.org/10.1007/s11036-015-0586-3.

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45

Carlsson, Gunnar. "Topological pattern recognition for point cloud data." Acta Numerica 23 (May 2014): 289–368. http://dx.doi.org/10.1017/s0962492914000051.

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In this paper we discuss the adaptation of the methods of homology from algebraic topology to the problem of pattern recognition in point cloud data sets. The method is referred to aspersistent homology, and has numerous applications to scientific problems. We discuss the definition and computation of homology in the standard setting of simplicial complexes and topological spaces, then show how one can obtain useful signatures, called barcodes, from finite metric spaces, thought of as sampled from a continuous object. We present several different cases where persistent homology is used, to illustrate the different ways in which the method can be applied.
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Chodroff, Eleanor, and Colin Wilson. "Mutual predictability among speech sounds for talker adaptation and recognition." Journal of the Acoustical Society of America 145, no. 3 (March 2019): 1689. http://dx.doi.org/10.1121/1.5101193.

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