Abstract
This study aims at providing audio-based interaction technology that lets the users have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. The paper presents the sound and speech analysis system evaluated thanks to a corpus of data acquired in a real smart home environment. The 4 steps of analysis are signal detection, speech/sound discrimination, sound classification and speech recognition. The results are presented for each step and globally. The very first experiments show promising results be it for the modules evaluated independently or for the whole system.
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References
Chan, M., Campo, E., Estève, D., Fourniols, J.Y.: Smart homes — current features and future perspectives. Maturitas 64(2), 90–97 (2009)
Weiser, M.: The computer for the 21st century. Scientific American 265(3), 66–75 (1991)
Vacher, M., Portet, F., Fleury, A., Noury, N.: Development of audio sensing technology for ambient assisted living: Applications and challenges. International Journal of E-Health and Medical Communications 2(1), 35–54 (2011)
Istrate, D., Vacher, M., Serignat, J.F.: Embedded implementation of distress situation identification through sound analysis. The Journal on Information Technology in Healthcare 6, 204–211 (2008)
Charalampos, D., Maglogiannis, I.: Enabling human status awareness in assistive environments based on advanced sound and motion data classification. In: Proceedings of the 1st International Conference on Pervasive Technologies Related to Assistive Environments, pp. 1:1–1:8 (2008)
Popescu, M., Li, Y., Skubic, M., Rantz, M.: An acoustic fall detector system that uses sound height information to reduce the false alarm rate. In: Proc. 30th Annual Int. Conference of the IEEE-EMBS 2008, August 20-25, pp. 4628–4631 (2008)
Badii, A., Boudy, J.: CompanionAble - integrated cognitive assistive & domotic companion robotic systems for ability & security. In: 1st Congres of the Société Française des Technologies pour l’Autonomie et de Gérontechnologie (SFTAG 2009), Troyes, pp. 18–20 (2009)
Hamill, M., Young, V., Boger, J., Mihailidis, A.: Development of an automated speech recognition interface for personal emergency response systems. Journal of Neuro Engineering and Rehabilitation 6 (2009)
Filho, G., Moir, T.J.: From science fiction to science fact: a smart-house interface using speech technology and a photo-realistic avatar. International Journal of Computer Applications in Technology 39(8), 32–39 (2010)
Lecouteux, B., Vacher, M., Portet, F.: Distant Speech Recognition in a Smart Home: Comparison of Several Multisource ASRs in Realistic Conditions. In: Interspeech 2011, Florence, Italy, p. 4 (August 2011)
Chen, J., Kam, A.H., Zhang, J., Liu, N., Shue, L.: Bathroom Activity Monitoring Based on Sound. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) PERVASIVE 2005. LNCS, vol. 3468, pp. 47–61. Springer, Heidelberg (2005)
Portet, F., Vacher, M., Golanski, C., Roux, C., Meillon, B.: Design and evaluation of a smart home voice interface for the elderly – acceptability and objection aspects. Personal and Ubiquitous Computing (in press)
Rougui, J., Istrate, D., Souidene, W.: Audio sound event identification for distress situations and context awareness. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, Minneapolis, USA, pp. 3501–3504 (2009)
Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems, vol. 11, pp. 487–493. MIT Press (1998)
Temko, A., Monte, E., Nadeu, C.: Comparison of sequence discriminant support vector machines for acoustic event classification. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (2005)
Wan, V., Renals, S.: Speaker verification using sequence discriminant support vector machines. IEEE Transactions on Speech and Audio Processing, 203–210 (2005)
Campbell, W.M., Sturim, D.E., Reynolds, D.A., Solomonoff, A.: SVM based speaker verification using a gmm supervector kernel and nap variability compensation. In: Proceedings of ICASSP 2006, pp. 97–100 (2006)
Fauve, B., Matrouf, D., Scheffer, N., Bonastre, J.F.: State-of-the-art performance in text-independent speaker verification through open-source software. IEEE Transactions on Audio, Speech, and Language Processing 15, 1960–1968 (2007)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov., 121–167 (1998)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press (2002)
Sehili, M.A., Istrate, D., Boudy, J.: Primary investigations of sound recognition for a domotic application using support vector. Automation, Computers, Electronics and Mechatronics, vol. 7(34(2)), pp. 61–65. Annals of the University of Craiova (2010)
Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted gaussian mixture models. In: Digital Signal Processing 2000 (2000)
Wölfel, M., McDonough, J.: Distant Speech Recognition, p. 573. John Wiley and Sons (2009)
Linarès, G., Nocéra, P., Massonié, D., Matrouf, D.: The LIA Speech Recognition System: From 10xRT to 1xRT. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 302–308. Springer, Heidelberg (2007)
Lecouteux, B., Linarès, G., Estève, Y., Gravier, G.: Generalized driven decoding for speech recognition system combination. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2008, pp. 1549–1552 (2008)
Lecouteux, B., Linarès, G., Bonastre, J., Nocéra, P.: Imperfect transcript driven speech recognition. In: InterSpeech 2006, pp. 1626–1629 (2006)
Logan, B.: Mel frequency cepstral coefficients for music modeling. In: Proceedings of International Symposium on Music Information Retrieval (2000)
Vacher, M., Lecouteux, B., Portet, F.: Recognition of Voice Commands by Multisource ASR and Noise Cancellation in a Smart Home Environment. In: EUSIPCO, Bucarest, Romania, pp. 1663–1667 (August 2012)
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Sehili, M.A. et al. (2012). Sound Environment Analysis in Smart Home. In: Paternò, F., de Ruyter, B., Markopoulos, P., Santoro, C., van Loenen, E., Luyten, K. (eds) Ambient Intelligence. AmI 2012. Lecture Notes in Computer Science, vol 7683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34898-3_14
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DOI: https://doi.org/10.1007/978-3-642-34898-3_14
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