Have a personal or library account? Click to login
Domain Adaptation of Deep Neural Networks for Automatic Speech Recognition via Wireless Sensors Cover

Domain Adaptation of Deep Neural Networks for Automatic Speech Recognition via Wireless Sensors

Journal of Electrical Engineering
Volume 67 (2016): Issue 2 (April 2016)
By:
Open Access
|May 2016

References

  1. [1] BURCHFIELD, T. R.-VENKATESAN, S. : Accelerometer- based Human Abnormal Movement Detection in Wireless Sensor Networks, Proceedings of ACM SIGMOBILE Workshop (2007), 67-69.10.1145/1248054.1248073
    Search in Google Scholar
  2. [2] HAYES, J.-BEIRNE, S.-LAU, K. T.-DIAMOND, D. : Evaluation of a Low Cost Wireless Chemical Sensor Network for Environmental Monitoring, IEEE Sensors Journal 64 No. 06 (2008), 530-533.
    Search in Google Scholar
  3. [3] GOGOLÁK, L.-PLETL, SZ.-KUKOLJ, D. : Neural Network- based Indoor Localization in WSN Environments, Acta Polytechnica Hungarica 10 No. 06 (2013), 221-235.
    Search in Google Scholar
  4. [4] GOGOLÁK, L.-KUKOLJ, D.-FÜRSTNER, I. : Wireless Sensor Network Based Localization in Industrial Environments, Analecta 8 No. 1 (2014), 91-96.
    Search in Google Scholar
  5. [5] GOSZTOLYA, G.-TÓTH, L. : Improving the Sound Recording Quality of Wireless Sensors Using Automatic Gain Control Methods, Scientific Bulletin of ”Politehnica” University of Timisoara, Transactions on Automatic Control and Computer Science 56 No. 2 (2011), 47-56.
    Search in Google Scholar
  6. [6] RABINER, L.-JUANG, B. H. : Fundamentals of Speech Recognition, Prentice Hall, Upper Saddle River, NJ, USA, 1993.
    Search in Google Scholar
  7. [7] FURUI, S. : Cepstral Analysis Technique for Automatic Speaker Verification, Acoustics, Speech and Signal 29 No. 2 (1981), 254-272.
    Search in Google Scholar
  8. [8] TÓTH, SZ. L.-SZTAHÓ, D.-VICSI, K. : Speech Emotion Perception by Human and Machine, Proceedings of COST Action (2012), 213-224.10.1007/978-3-540-70872-8_16
    Search in Google Scholar
  9. [9] GOSZTOLYA, G.-BUSA-FEKETE, R.-TÓTH, L. : Detecting Autism, Emotions and Social Signals Using AdaBoost, Proceedings of Interspeech (2013), 220-224.10.21437/Interspeech.2013-71
    Search in Google Scholar
  10. [10] MORGAN, M.-BOURLARD, H. : An Introduction to Hybrid HMM/Connectionist Continuous Speech Recognition, Signal Processing Magazine (May 1995), 1025-1028.
    Search in Google Scholar
  11. [11] NEDERHOF, M.-J. : Practical experiments with regular approximation of context-free languages, Journal of Computational Linguistics 26 No. 1 (2000), 17-44.10.1162/089120100561610
    Search in Google Scholar
  12. [12] VARGA, I.-OHTAKE, K.-TORISAWA, K.-DESAEGER, S.-MISU, T.-MATSUDA, S.-KAZAMA, J. : Similarity Based Language Model Construction for Voice Activated Open- Domain Question Answering, Proceedings of IJCNLP (2011), 535-544.
    Search in Google Scholar
  13. [13] DUDA, R. O.-HART, P. E. : Pattern Classification and Scene Analysis, John Wiley & Sons, New Jersey, 1973.
    Search in Google Scholar
  14. [14] HINTON, G. E.-OSINDERO, S.-TEH, Y.-W. : A Fast Learning Algorithm for Deep Belief Nets, Neural Computation 18 No. 7 (2006), 1527-1554.10.1162/neco.2006.18.7.152716764513
    Search in Google Scholar
  15. [15] SEIDE, F.-LI, G.-CHEN, X.-YU, D. : Feature Engineering in Context-Dependent Deep Neural Networks for Conversational Speech Transcription, Proceedings of ASRU (2011), 24-29.10.1109/ASRU.2011.6163899
    Search in Google Scholar
  16. [16] BENGIO, Y.-LAMBLIN, P.-POPOVICI, D.-LAROCHELLE, H. : Greedy Layer-Wise Training of Deep Networks, Advances in Neural Information Processing Systems 19 (2007), 153-160.
    Search in Google Scholar
  17. [17] GLOROT, X.-BORDES, A.-BENGIO, Y. : Deep Sparse Rectifier Networks, Proceedings of AISTATS (2011), 315-323.
    Search in Google Scholar
  18. [18] GRÓSZ, T.-TÓTH, L. : A Comparison of Deep Neural Network Training Methods for Large Vocabulary Speech Recognition, Proceedings of TSD (2013), 36-43.10.1007/978-3-642-40585-3_6
    Search in Google Scholar
  19. [19] TÓTH, L. : Phone Recognition with Deep Sparse Rectifier Neural Networks, Proceedings of ICASSP (2013), 6985-6989.10.1109/ICASSP.2013.6639016
    Search in Google Scholar
  20. [20] SELTZER, M.-YU, D.-WANG, Y. : An Investigation of Deep Neural Networks for Noise Robust Speech Recognition, Proceedings of ICASSP (2013), 7398-7402.10.1109/ICASSP.2013.6639100
    Search in Google Scholar
  21. [21] KOVÁCS, GY.-TÓTH, L. : Joint Optimization of Spectro- Temporal Features and Deep Neural Nets for Robust Automatic Speech Recognition, Acta Cybernetica 22 No. 1 (2015), 117-134.10.14232/actacyb.22.1.2015.8
    Search in Google Scholar
  22. [22] JAIN, P.-HERMANSKY, H.-KINGSBURY, B. : Distributed Speech Recognition Using Noise-Robust MFCC and TRAPSestimated Manner Features, Proceedings of Interspeech (2002), 473-476.10.21437/ICSLP.2002-22
    Search in Google Scholar
  23. [23] AGARWAL, A.-CHENG, Y. M. : Two-Stage Mel-Warped Wiener Filter For Robust Speech Recognition, Proceedings of ASRU (1999), 12-15.
    Search in Google Scholar
  24. [24] GAO, T.-DU, J.-DAI, L.-R.-LEE, C.-H. : Joint Training of Front-end and Back-end Deep Neural Networks for Robust Speech Recognition, Proceedings of ICASSP (2015), 4375-4379.10.1109/ICASSP.2015.7178797
    Search in Google Scholar
  25. [25] LIAO, H.-GALES, M. J. F. : Adaptive Training with Joint Uncertainty Decoding for Robust Recognition of Noisy Data, Proceedings of ICASSP (2007), 389-392.10.1109/ICASSP.2007.366931
    Search in Google Scholar
  26. [26] HUANG, Y.-SLANEY, M.-SELTZER, M. L.-GONG, Y. : Towards Better Performance with Heterogeneous Training Data in Acoustic Modeling Using Deep Neural Networks, Proceedings of Interspeech (2015), 845-849.10.21437/Interspeech.2014-214
    Search in Google Scholar
  27. [27] YOUNG, S.-EVERMANN, G.-GALES, M. J. F.-HAIN, T.-KERSHAW, D.-MOORE, G.-ODELL, J.-OLLASON, D.-POVEY, D.-VALTCHEV, V.-WOODLAND, P. C. : The HTK Book, Cambridge University Engineering Department, Cambridge, UK, 2006.
    Search in Google Scholar
  28. [28] ABARI, K.-OLASZY, G.-ZAINKÓ, CS.-KISS, G. : Hungarian Pronunciation Dictionary on Internet (in Hungarian), Proceedings of MSZNY (2006), 223-230.
    Search in Google Scholar
  29. [29] TÓTH, L. : Phone Recognition with Hierarchical Convolutional Deep Maxout Networks, EURASIP Journal on Audio, Speech, and Music Processing 2015 No. 25 (2015), 1-13.
    Search in Google Scholar
  30. [30] GRÓSZ, T.-BUSA-FEKETE, R.-GOSZTOLYA, G.-TÓTH, L. : Assessing the Degree of Nativeness and Parkinson’s Condition Using Gaussian Processes and Deep Rectifier Neural Networks, Proceedings of Interspeech (2015), 1339-1343.10.21437/Interspeech.2015-195
    Search in Google Scholar
  31. [31] GOSZTOLYA, G.-GRÓSZ, T.-TÓTH, L.-IMSENG, D. : Building Context-Dependent DNN Acousitc Models Using Kullback- Leibler Divergence-Based State Tying, Proceedings of ICASSP (2015), 4570-4574.10.1109/ICASSP.2015.7178836
    Search in Google Scholar
DOI: https://doi.org/10.1515/jee-2016-0017 | Journal eISSN: 1339-309X | Journal ISSN: 1335-3632
Journal RSS Feed
Language: English
Page range: 124 - 130
Submitted on: Nov 20, 2015
Published on: May 14, 2016
Published by: Slovak University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 6 times per year
Keywords:
wireless sensors,
speech recognition,
deep neural networks,
domain adaptation
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2016 Gábor Gosztolya, Tamás Grósz, published by Slovak University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous articleVolume 67 (2016): Issue 2 (April 2016)Next article