Distant speech recognition in smart homes initiated by hand clapping within noisy environments
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Distant Speech Recognition in Smart Homes Initiated by Hand Clapping within Noisy Environments. Florian Bacher & Christophe Sourisse. [623.400] Seminar in Interactive Systems. Agenda. Introduction Methodology Experiment Description Implementation Results Conclusion.

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Distant Speech Recognition in Smart Homes Initiated by Hand Clapping within Noisy Environments .

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Distant Speech Recognition in Smart Homes Initiated by Hand Clapping within Noisy Environments.

Florian Bacher & Christophe Sourisse

[623.400] Seminar in Interactive Systems


  • Introduction

  • Methodology

  • Experiment Description

  • Implementation

  • Results

  • Conclusion

I. Introduction


  • Smart homes have become a major field of research in information and communication technologies.

  • Possible way of interaction: Voice commands.

  • Goal of our experiment:evaluate the possibility of recognizing voice commands initiated by hand claps in a noisy environment.

  • Gather a set of voice commands uttered by various speakers.

II. Methodology


  • Main method: Lecouteux et al. [1]

    • Deals with speech recognition within distress situations.

    • Problem: no background noise was considered.

  • Chosen methodology: adapt Lecouteux et al. protocol considering:

    • Noisy settings.

    • Initiating recognition using hand claps.

Methodological issues

  • Choice of the room setting

    • Lecouteux et al. [1]: a whole flat.

    • Vovos et al. [7]: one-room microphone array.

    • Choice: one room with 2 microphones.

  • Choice of background noises

    • Hirsch and Pierce [8]: NoiseX 92 database.

    • Moncrieff et al. [5]: “Background noise is defined as consisting of typical regularly occurring sounds.”

    • Choice: background noises of the daily house life.

III. Experiment Description

Experiment Settings

  • Performed in a 3m x 3m room.

  • Sounds were captured by two microphones which were hidden in the room.

Experimental Protocol

  • 20 participants (10 men, 10 women, 25,5 ± 11 years) participated to a 2-phase exp.

  • 1st phase: recognize a word (“Jeeves”) as a command

    • System’s attention is catched by double clapping.

    • 4 scenarios.

    • Background noises tested: step noises, opening doors, moving chairs, radio show.

  • 2nd phase: Gather a set of voicecommands

    • List of 15 command-words.

    • Reference record for pronounciation issues.

    • Eachwordisuttered 10 times.

IV. Implementation


  • Used technologies:

    • C# Library System.Speech.Recognition: Interface to the Speech Recognition used by Windows.

    • Microphones: Two dynamic microphones with cardioid polar pattern (Sennheiser BF812/e8155)

    • Line6 UX1 Audio Interface

    • Line6 Pod Farm 2.5


  • Signal is captured in real time.

  • If there are exactly two signal peaks within a certain timeframe, the software classifies them as a double clap.

  • After a double clap has been detected, the actual speech recognition engine is activated (i.e. the software is waiting for commands).

V. Results

Results’ Classification

General Results

Detailed Results

VI. Conclusion


  • A new idea of how to initiate speech recognition in human computer interaction.

  • An evaluation of the potential influence of a noisy environment.

  • Results: encouraging, but not yet satisfying.

  • Next step: perform this experiment in a real smart-home-context.


  • [1] B. Lecouteux, M. Vacher and F. Portet. Distant speech recognition in a smart home: comparison of several multisouce ASRs in realistic conditions. Interspeech., 2011.

  • [2] A. Fleury, N. Noury, M. Vacher, H. Glasson and J.-F. Serignat. Sound and speech detection and classification in a health smart home. 30th Annual International IEEE EMBS Conference, Vancouver, British Columbia, Canada, August 2008.

  • [3] M. Vacher, N. Guirand, J.-F. Serignat and A. Fleury. Speech recognition in a smart home: Some experiments for telemonitoring. Proceedings of the 5th Conference on Speech Technology and Human-Computer Dialogue, pages 1 – 10, June 2009.

  • [4] J. Rouillard and J.-C. Tarby. How to communicate smartly with your house? Int. J. Ad Hoc and Ubiquitous Computing, 7(3), 2011.

  • [5] S. Moncrieff, S. Venkatesh, G. West, and S. Greenhill. Incorporating contextual audio for an actively anxious smart home. Proceedings of the 2005 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pages 373 – 378, Dec. 2005.

  • [6] M. Vacher, D. Istrate, F. Portet, T. Joubert, T. Chevalier, S. Smidtas, B. Meillon, B. Lecouteux, M. Sehili, P. Chahuara and S. Méniard. The sweet-home project: Audio technology in smart homes to improve well-being and reliance. 33rd Annual International IEEE EMBS Conference, Boston, Massachusetts, USA, 2011.

  • [7] A. Vovos, B. Kladis and N. Fakotakis, Speech operated smart-home control system for userswithspecialneeds, in Proc. Interspeech 2005, 2005, pp. 193 – 196.

  • [8] H.-G. Hirsch and D. Pearce. The AURORA experimentalframework for the performance evaluation of speech recognition systemsundernoisy conditions. In ASR-2000, pages 181 – 188.

Thank you for your attention!


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