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Distributed Speech Recognition for Lighting System Control

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Intelligent Decision Technologies (IDT 2017)

Abstract

This paper presents a distributed speech recognition (DSR) system for home/office lighting control by means of users’ voice. In this scheme a back-end processes audio signals and transforms them into commands, so that they can be sent to the desired actuators of the lighting system. This paper discusses in detail the solutions and strategies we adopted to improve recognition accuracy and spotting command efficiency in home/office environments, i.e. in situations that involve distant speech and great amounts of background noise or unrelated sounds. Suitable solutions implemented in this recognition engine are able to detect commands also in a continuous listening context and the used DSR strategies greatly simplify the system installation and maintenance. A case study that implements the voice control of a digital addressable lighting interface (DALI) based lighting system has been selected to show the validity and the performance of the proposed system.

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References

  1. Alessandrini, M., Biagetti, G., Curzi, A., Turchetti, C.: Semi-automatic acoustic model generation from large unsynchronized audio and text chunks. In: Twelfth Annual Conference of the International Speech Communication Association (2011)

    Google Scholar 

  2. Alessandrini, M., Biagetti, G., Curzi, A., Turchetti, C.: A garbage model generation technique for embedded speech recognisers. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 318–322 (2013)

    Google Scholar 

  3. Alessandrini, M., Biagetti, G., Curzi, A., Turchetti, C.: A speech interaction system for an ambient assisted living scenario. In: Ambient Assisted Living, pp. 233–239. Springer (2014)

    Google Scholar 

  4. Anastasiou, D.: Survey on speech, machine translation and gestures in ambient assisted living. In: Tralogy, Session 4—Tools for translators. Paris, France (2012)

    Google Scholar 

  5. Bazzi, I., Glass, J.R.: Modeling out-of-vocabulary words for robust speech recognition. In: Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP 2000 / INTERSPEECH 2000), pp. 401–404. Beijing, China (Oct 2000)

    Google Scholar 

  6. Biagetti, G., Crippa, P., Curzi, A., Orcioni, S., Turchetti, C.: Speaker identification with short sequences of speech frames. In: International Conference on Pattern Recognition Applications and Methods (ICPRAM 2015), pp. 178–185 (Jan 2015)

    Google Scholar 

  7. ETSI ES 202 050 V1.1.5: Speech Processing, Transmission and Quality Aspects (STQ); Distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms (Jan 2007)

    Google Scholar 

  8. Hirota, S., Hayasaka, N., Iiguni, Y.: Experimental evaluation of structure of garbage model generated from in-vocabulary words. In: Proceedings of the 2012 International Symposium on Communications and Information Technologies (ISCIT 2012), pp. 87–92. Gold Coast, Australia (Oct 2012)

    Google Scholar 

  9. Levit, M., Chang, S., Buntschuh, B.: Garbage modeling with decoys for a sequential recognition scenario. In: Procedings of the 11th IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU 2009), pp. 468–473. Merano, Italy (Dec 2009)

    Google Scholar 

  10. Pearce, D.: Enabling new speech driven services for mobile devices: An overview of the ETSI standards activities for distributed speech recognition front-ends. In: AVIOS 2000: The Speech Applications Conference, pp. 261–264 (2000)

    Google Scholar 

  11. Picone, J.: Continuous speech recognition using hidden Markov models. ASSP Mag. IEEE 7(3), 26–41 (1990)

    Article  Google Scholar 

  12. Seltzer, M., Singh, R.: Instructions for using the Sphinx3 trainer, http://www.speech.cs.cmu.edu/sphinxman/fr4.html

  13. Vacher, M., Portet, F., Fleury, A., Noury, N.: Challenges in the processing of audio channels for ambient assisted living. In: Proceedings of the 12th IEEE International Conference on e-Health Networking Applications and Services (Healthcom’10), pp. 330–337. Lyon, France (Jul 2010)

    Google Scholar 

  14. Walker, W., Lamere, P., Kwok, P., Raj, B., Singh, R., Gouvea, E., Wolf, P., Woelfel, J.: Sphinx-4: A flexible open source framework for speech recognition. Technical Report SMLI TR2004-0811, Sun Microsystems Inc. (2004)

    Google Scholar 

  15. Zhang, W., He, L., Chow, Y.L., Yang, R., Su, Y.: The study on distributed speech recognition system. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 1431–1434. IEEE (2000)

    Google Scholar 

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Correspondence to Giorgio Biagetti .

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Biagetti, G., Crippa, P., Curzi, A., Falaschetti, L., Orcioni, S., Turchetti, C. (2015). Distributed Speech Recognition for Lighting System Control. In: Neves-Silva, R., Jain, L., Howlett, R. (eds) Intelligent Decision Technologies. IDT 2017. Smart Innovation, Systems and Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-19857-6_10

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  • DOI: https://doi.org/10.1007/978-3-319-19857-6_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19856-9

  • Online ISBN: 978-3-319-19857-6

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