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Detection and Speech/Sound Segmentation in a Smart Room Environment


Because of cost or convenience reasons, patients or elderly people would be hospitalized at home and smart information system would be needed in order to assist human operators. In this case, physiologic and position sensors give already numerous informations, but there are few studies for sound use in patient's habitation. However, sound classification and speech recognition may greatly increase the versatility of such a system: this will be provided by detecting specific sounds or short sentences which could characterize a distress situation for the patient. Sounds emitted in patient's habitation may be useful for patient's activity monitoring. The proposed sound analysis system is made of four modules: the first module in charge of sound and speech extraction is the detection module, it is followed by a segmentation module needed to transmit the extracted wave to the Sound Classification module or to the Speech Recognition module. The first two modules -Detection and Segmentation- are presented and evaluated in this paper in experimental recorded noise conditions. The detection method uses transient models, based upon dyadic trees of wavelet coefficients to insure short detection delay. The segmentation step is a classical Gaussian Mixture Model classifier based on acoustical parameters like MFCC.
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hal-01092602 , version 1 (09-12-2014)


  • HAL Id : hal-01092602 , version 1


Michel Vacher, Dan Istrate, Jean-François Serignat, Nicolas Gac. Detection and Speech/Sound Segmentation in a Smart Room Environment. Trends in Speech Technology, The 3rd International Conference on Speech Technology and Human-Computer Dialogue, IEEE, SpeD2005, May 2005, Cluj, Romania. ⟨hal-01092602⟩
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