Speech and Speaker Recognition for Home Automation: Preliminary Results

Abstract : In voice controlled multi-room smart homes ASR and speaker identification systems face distance speech conditions which have a significant impact on performance. Regarding voice command recognition, this paper presents an approach which selects dynamically the best channel and adapts models to the environmental conditions. The method has been tested on data recorded with 11 elderly and visually impaired participants in a real smart home. The voice command recognition error rate was 3.2% in off-line condition and of 13.2% in online condition. For speaker identification, the performances were below very speaker dependant. However, we show a high correlation between performance and training size. The main difficulty was the too short utterance duration in comparison to state of the art studies. Moreover, speaker identification performance depends on the size of the adapting corpus and then users must record enough data before using the system.
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Contributor : Michel Vacher <>
Submitted on : Tuesday, October 27, 2015 - 3:21:00 PM
Last modification on : Monday, February 11, 2019 - 4:36:02 PM
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  • HAL Id : hal-01207692, version 2



Michel Vacher, Benjamin Lecouteux, Javier Serrano-Romero, Moez Ajili, François Portet, et al.. Speech and Speaker Recognition for Home Automation: Preliminary Results. 8th International Conference Speech Technology and Human-Computer Dialogue "SpeD 2015", Oct 2015, Bucarest, Romania. pp.181-190. ⟨hal-01207692v2⟩



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