Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

12. Representation Sharing and Transfer in Deep Neural Networks

verfasst von : Dong Yu, Li Deng

Erschienen in: Automatic Speech Recognition

Verlag: Springer London

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We have emphasized in the previous chapters that in deep neural networks (DNNs) each hidden layer is a new representation of the raw input to the DNN. The representation at higher layers is more abstract than that at lower layers. In this chapter, we show that these feature representations can be shared and transferred across related tasks through techniques such as multitask and transfer learning. We will use multilingual and crosslingual speech recognition as the main example, which uses a shared-hidden-layer DNN architecture, to demonstrate these techniques.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Adaptation of Deep Neural Networks
Nächstes Kapitel Recurrent Neural Networks and Related Models
Literatur
1.
Association, I.P., et al.: Report on the 1989 Kiel convention. J. Int. Phonetic Assoc. 19(2), 67–80 (1989)CrossRef
2.
Athineos, M., Ellis, D.P.: Frequency-domain linear prediction for temporal features. In: Proceedings of the IEEE Workshop on Automfatic Speech Recognition and Understanding (ASRU), pp. 261–266 (2003)
3.
4.
Chen, D., Mak, B., Leung, C.C., Sivadas, S.:  Joint acoustic modeling of triphones and trigraphemes by multi-task learning deep neural networks for low-resource speech recognition. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014)
5.
Chen, T., Rao, R.R.: Audio-visual integration in multimodal communication. Proc. IEEE 86(5), 837–852 (1998)CrossRefMathSciNet
6.
Chibelushi, C.C., Deravi, F., Mason, J.S.: A review of speech-based bimodal recognition. Multimedia IEEE Trans. 4(1), 23–37 (2002)
7.
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012)CrossRef
8.
Dupont, S., Luettin, J.: Audio-visual speech modeling for continuous speech recognition. Multimedia IEEE Trans. 2(3), 141–151 (2000)CrossRef
9.
Fiscus, J.G.: A post-processing system to yield reduced word error rates: recognizer output voting error reduction (ROVER). In: Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 347–354 (1997)
10.
Garofolo, J.S.: Darpa Timit: Acoustic-Phonetic Continuous Speech Corps CD-ROM. US Department of Commerce, National Institute of Standards and Technology, Gaithersburg (1993)
11.
Ghoshal, A., Swietojanski, P., Renals, S.: Multilingual training of deep-neural netowrks. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2013)
12.
Heigold, G., Vanhoucke, V., Senior, A., Nguyen, P., Ranzato, M., Devin, M., Dean, J.: Multilingual acoustic models using distributed deep neural networks. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2013)
13.
Hermansky, H.: Perceptual linear predictive (PLP) analysis of speech. J. Acoust. Soc. Am. 87, 1738 (1990)CrossRef
14.
Hinton, G., Deng, L., Yu, D., Dahl, G.E.,Mohamed, A.r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)
15.
Huang, J., Kingsbury, B.: Audio-visual deep learning for noise robust speech recognition. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7596–7599 (2013)
16.
Huang, J.T., Li, J., Yu, D., Deng, L., Gong, Y.: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2013)
17.
Kim, M.W., Ryu, J.W., Kim, E.J.: Speech recognition by integrating audio, visual and contextual features based on neural networks. Advances in Natural Computation, pp. 155–164. Springer, Berlin (2005)CrossRef
18.
Lee, K.F., Hon, H.W.: Speaker-independent phone recognition using hidden Markov models. IEEE Trans. Speech Audio Process. 37(11), 1641–1648 (1989)CrossRef
19.
Lewis, T.W., Powers, D.M.: Audio-visual speech recognition using red exclusion and neural networks. J. Res. Pract. Inf. Technol. 35(1), 41–64 (2003)
20.
Lin, H., Deng, L., Yu, D., Gong, Y.f., Acero, A., Lee, C.H.: A study on multilingual acoustic modeling for large vocabulary ASR. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4333–4336 (2009)
21.
Lu, Y., Lu, F., Sehgal, S., Gupta, S., Du, J., Tham, C.H., Green, P., Wan, V.: Multitask learning in connectionist speech recognition. In: Proceedings of the  Australian International Conference on Speech Science and Technology (2004)
22.
Martens, J.: Deep learning via Hessian-free optimization. In: Proceedings of the International Conference on Machine Learning (ICML), pp. 735–742 (2010)
23.
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 689–696 (2011)
24.
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRef
25.
Plahl, C., Schluter, R., Ney, H.: Cross-lingual portability of chinese and english neural network features for french and german LVCSR.In: Proceedings of the IEEE Workshop on Automfatic Speech Recognition and Understanding (ASRU), pp. 371–376 (2011)
26.
Potamianos, G., Neti, C., Gravier, G., Garg, A., Senior, A.W.: Recent advances in the automatic recognition of audiovisual speech. Proc. IEEE 91(9), 1306–1326 (2003)CrossRef
27.
Qian, Y., Liu, J.: Cross-lingual and ensemble MLPs strategies for low-resource speech recognition. In: Proceedings of the Annual Conference of International Speech Communication Association (INTERSPEECH) (2012)
28.
Schultz, T., Waibel, A.: Multilingual and crosslingual speech recognition. In: Proceedings of the DARPA Workshop on Broadcast News Transcription and Understanding, pp. 259–262 (1998)
29.
Seide, F., Li, G., Yu, D.: Conversational speech transcription using context-dependent deep neural networks. In: Proceedings of the Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 437–440 (2011)
30.
Seltzer, M.L., Droppo, J.: Multi-task learning in deep neural networks for improved phoneme recognition. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6965–6969 (2013)
31.
Sumby, W.H., Pollack, I.: Visual contribution to speech intelligibility in noise. J. Acoust. Soc. Am. (JASA) 26(2), 212–215 (1954)CrossRef
32.
Thomas, S., Ganapathy, S., Hermansky, H.: Cross-lingual and multi-stream posterior features for low resource LVCSR systems. In: Proceedings of the Annual Conference of International Speech Communication Association (INTERSPEECH), pp. 877–880 (2010)
33.
Thomas, S., Ganapathy, S., Hermansky, H.: Multilingual MLP features for low-resource LVCSR systems. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4269–4272 (2012)
34.
Yu, D., Deng, L., Liu, P., Wu, J., Gong, Y., Acero, A.: Cross-lingual speech recognition under runtime resource constraints. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4193–4196 (2009)
Metadaten
Titel
Representation Sharing and Transfer in Deep Neural Networks
verfasst von
Dong Yu
Li Deng
Copyright-Jahr
2015
Verlag
Springer London
Buch
Automatic Speech Recognition

Print ISBN: 978-1-4471-5778-6

Electronic ISBN: 978-1-4471-5779-3

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-1-4471-5779-3_12

Neuer Inhalt

    Bildnachweise