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

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Статті в журналах з теми "Cloud speech recognition adaptation"

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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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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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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Дисертації з теми "Cloud speech recognition adaptation"

1

Chan, Carlos Chun Ming. "Speaker model adaptation in automatic speech recognition." Thesis, Robert Gordon University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339307.

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2

Ho, Ka-Lung. "Kernel eigenvoice speaker adaptation /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20HOK.

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Анотація:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 56-61). Also available in electronic version. Access restricted to campus users.
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3

Humphries, J. J. "Accent modelling and adaptation in automatic speech recognition." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.604784.

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Automatic speech recognition technology has advanced considerably and today's systems are sufficiently fast, affordable and robust to be useful in a wide range of applications. As the scope of these products increases, so does the range of people using them. The diversity of speaker accents which this brings poses a serious problem for existing technology. Speech recognition systems are generally trained on a specific accent group, such as Standard British English (RP). This work demonstrates that the performance of such systems deteriorates significantly when the accent of the incoming speech is different to that represented by the recogniser (typically more than a 200% increase in word error rate). It is shown that this is attributable to both acoustic and phonological differences between accents. Pronunciation modelling can help overcome phonological differences and a new scheme is described which builds upon previous work in this area to give a fully automated method for capturing pronunciation variations. This method requires no linguistic intervention and works with modern large vocabulary continuous speech recognisers. An existing (e.g. British) phone-loop recogniser transcribes speech from the new accent region (e.g. American) and the resulting phonetic transcription compared to standard (British) pronunciations. The phonological differences are then recorded, along with their phonetic context and confidence scores. A statistical pronunciation model of the new accent can then be produced by clustering these observations using binary decision trees. From these, a set of multiple pronunciations, appropriate to the new accent and with associated probabilities, can be generated and used within the original speech recogniser. This technique has been applied to the speech recognition problem in a range of adaptation and re-training scenarios, using American, British and non-native English speech data, and has been shown to reduce recogniser word error rates by up to 20%. Pronunciation effects captured in an automatically generated model of American English are shown to agree well with linguistic theory, and the similarity of pronunciations synthesised from this model to canonical American pronunciations is shown. A scheme for the integration of pronunciation adaptation with acoustic adaptation (specially MLLR) has also been presented and shown to be effective in producing reductions in recogniser word error rates of as much as 40%. The value of syllable and cross-word information in the accent model was also evaluated.
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4

Cox, S. J. "Techniques for rapid speaker adaptation in speech recognition." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267271.

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5

Nieuwoudt, Christoph. "Cross-language acoustic adaptation for automatic speech recognition." Thesis, Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-01062005-071829.

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6

Hewett, Andrew John. "Training and speaker adaptation in template-based speech recognition." Thesis, University of Cambridge, 1989. https://www.repository.cam.ac.uk/handle/1810/250961.

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7

Clarkson, P. R. "Adaptation of statistical language models for automatic speech recognition." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597745.

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Statistical language models encode linguistic information in such a way as to be useful to systems which process human language. Such systems include those for optical character recognition and machine translation. Currently, however, the most common application of language modelling is in automatic speech recognition, and it is this that forms the focus of this thesis. Most current speech recognition systems are dedicated to one specific task (for example, the recognition of broadcast news), and thus use a language model which has been trained on text which is appropriate to that task. If, however, one wants to perform recognition on more general language, then creating an appropriate language model is far from straightforward. A task-specific language model will often perform very badly on language from a different domain, whereas a model trained on text from many diverse styles of language might perform better in general, but will not be especially well suited to any particular domain. Thus the idea of an adaptive language model whose parameters automatically adjust to the current style of language is an appealing one. In this thesis, two adaptive language models are investigated. The first is a mixture-based model. The training text is partitioned according to the style of text, and a separate language model is constructed for each component. Each component is assigned a weighting according to its performance at modelling the observed text, and a final language model is constructed as the weighted sum of each of the mixture components. The second approach is based on a cache of recent words. Previous work has shown that words that have occurred recently have a higher probability of occurring in the immediate future than would be predicted by a standard triagram language model. This thesis investigates the hypothesis that more recent words should be considered more significant within the cache by implementing a cache in which a word's recurrence probability decays exponentially over time. The problem of how to predict the effect of a particular language model on speech recognition accuracy is also addressed in this thesis. The results presented here, as well as those of other recent research, suggest that perplexity, the most commonly used method of evaluating language models, is not as well correlated with word error rate as was once thought. This thesis investigates the connection between a language model's perplexity and its effect on speech recognition performance, and will describe the development of alternative measures of a language models' quality which are better correlated with word error rate. Finally, it is shown how the recognition performance which is achieved using mixture-based language models can be improved by optimising the mixture weights with respect to these new measures.
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8

Ge, Zhenhao. "Mispronunciation detection for language learning and speech recognition adaptation." Thesis, Purdue University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3613127.

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The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.

There are a number of Computer Aided Language Learning (CALL) systems with Computer Aided Pronunciation Training (CAPT) techniques that have been developed. In this thesis, a new HMM-based text-dependent mispronunciation system is introduced using text Adaptive Frequency Cepstral Coefficients (AFCCs). It is shown that this system outperforms the conventional HMM method based on Mel Frequency Cepstral Coefficients (MFCCs). In addition, a mispronunciation detection and classification algorithm based on Principle Component Analysis (PCA) is introduced to help language learners identify and correct their pronunciation errors at the word and syllable levels.

To improve speech recognition by adaptation, two projects have been explored. The first one improves name recognition by learning acceptable variations in name pronunciations, as one of the approaches to make grammar-based name recognition adaptive. The second project is accent detection by examining the shifting of fundamental vowels in accented speech. This approach uses both acoustic and phonetic information to detect accents and is shown to be beneficial with accented English. These applications can be integrated into an automated international calling system, to improve recognition of callers' names and speech. It determines the callers' accent based in a short period of speech. Once the type of accents is detected, it switches from the standard speech recognition engine to an accent-adaptive one for better recognition results.

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9

Uebel, Luís Felipe. "Speaker normalisation and adaptation in large vocabulary speech recognition." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616207.

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10

McInnes, Fergus Robert. "Adaptation of reference patterns in word-based speech recognition." Thesis, University of Edinburgh, 1988. http://hdl.handle.net/1842/12618.

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Книги з теми "Cloud speech recognition adaptation"

1

Goronzy, Silke, ed. Robust Adaptation to Non-Native Accents in Automatic Speech Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36290-8.

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2

Robust adaptation to non-native accents in automatic speech recognition. Berlin: Springer, 2002.

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3

Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, and acoustic and language models, which are estimated in a training phase, and the decoder. The focus of this article is on methods used in state-of-the-art speaker-independent, large-vocabulary continuous speech recognition (LVCSR). Primary application areas for such technology are dictation, spoken language dialogue, and transcription for information archival and retrieval systems. Finally, this article discusses issues and directions of future research.
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4

Goronzy, Silke. Robust Adaptation to Non-Native Accents in Automatic Speech Recognition. Springer, 2003.

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5

Goronzy, Silke. Robust Adaptation to Non-Native Accents in Automatic Speech Recognition. Springer, 2003.

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Частини книг з теми "Cloud speech recognition adaptation"

1

Lee, Kai-Fu. "Learning and Adaptation." In Automatic Speech Recognition, 115–27. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-3650-5_7.

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2

Yu, Dong, and Li Deng. "Adaptation of Deep Neural Networks." In Automatic Speech Recognition, 193–215. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_11.

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3

Schwartz, Richard, and Francis Kubala. "Hidden Markov Models and Speaker Adaptation." In Speech Recognition and Understanding, 31–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76626-8_2.

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4

Kamath, Uday, John Liu, and James Whitaker. "Transfer Learning: Domain Adaptation." In Deep Learning for NLP and Speech Recognition, 495–535. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14596-5_11.

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5

Brignatz, Vincent, Jarod Duret, Driss Matrouf, and Mickael Rouvier. "Language Adaptation for Speaker Recognition Systems Using Contrastive Learning." In Speech and Computer, 91–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87802-3_9.

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6

Soe Naing, Hay Mar, and Win Pa Pa. "Speaker Adaptation on Myanmar Spontaneous Speech Recognition." In Communications in Computer and Information Science, 303–13. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8438-6_24.

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Bhat, Chitralekha, Bhavik Vachhani, and Sunil Kopparapu. "Improving Recognition of Dysarthric Speech Using Severity Based Tempo Adaptation." In Speech and Computer, 370–77. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43958-7_44.

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8

Sun, Huiyu, Ralph Grishman, and Yingchao Wang. "Domain Adaptation with Active Learning for Named Entity Recognition." In Cloud Computing and Security, 611–22. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48674-1_54.

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Rudzionis, Vytautas, Rytis Maskeliunas, Algimantas Rudzionis, and Kastytis Ratkevicius. "On the Adaptation of Foreign Language Speech Recognition Engines for Lithuanian Speech Recognition." In Business Information Systems Workshops, 113–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03424-4_13.

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Wen, Yu, Ke Yao, Chunlin Tian, Yao Wu, Zhongmin Zhang, Yaning Shi, Yin Tian, Jin Yang, and Peiqi Wang. "Aggregated Multimodal Bidirectional Recurrent Model for Audiovisual Speech Recognition." In Cloud Computing and Security, 380–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00021-9_35.

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Тези доповідей конференцій з теми "Cloud speech recognition adaptation"

1

Raut, C. K., and M. J. F. Gales. "Bayesian discriminative adaptation for speech recognition." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4960595.

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2

Mertens, Timo, Daniel Schneider, Arild Brandrud Naess, and Torbjorn Svendsen. "Lexicon adaptation for subword speech recognition." In 2009 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU 2009). IEEE, 2009. http://dx.doi.org/10.1109/asru.2009.5373296.

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3

Ren, Bo, Longbiao Wang, Atsuhiko Kai, and Zhaofeng Zhang. "Speech selection and environmental adaptation for asynchronous speech recognition." In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). IEEE, 2015. http://dx.doi.org/10.1109/apsipa.2015.7415485.

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4

Rennie, Steven, Pierre Dognin, and Petr Fousek. "Robust speech recognition using dynamic noise adaptation." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947377.

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Kozat, Suleyman S., Karthik Visweswariah, and Ramesh Gopinath. "Efficient, Low Latency Adaptation for Speech Recognition." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.367028.

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Li, Jinyu, Yu Tsao, and Chin-Hui Lee. "Shrinkage model adaptation in automatic speech recognition." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-478.

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Sokolov, Artem, and Andrey V. Savchenko. "Gender domain adaptation for automatic speech recognition." In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2021. http://dx.doi.org/10.1109/sami50585.2021.9378626.

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Ghalehjegh, Sina Hamidi, and Richard C. Rose. "Phonetic subspace adaptation for automatic speech recognition." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6639210.

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Potamianos, Gerasimos, and Alexandros Potamianos. "Speaker adaptation for audio-visual speech recognition." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA: ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-303.

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Beaugendre, Frédéric, Tom Claes, and Hugo van Hamme. "Dialect adaptation for Mandarin Chinese speech recognition." In 6th International Conference on Spoken Language Processing (ICSLP 2000). ISCA: ISCA, 2000. http://dx.doi.org/10.21437/icslp.2000-391.

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Звіти організацій з теми "Cloud speech recognition adaptation"

1

Hon, Hsiao-Wuen, and Kai-Fu Lee. Vocabulary and Environment Adaptation in Vocabulary-Independent Speech Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1992. http://dx.doi.org/10.21236/ada457730.

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