Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies 2015
DOI: 10.18653/v1/w15-5121
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Recognition of Distress Calls in Distant Speech Setting: a Preliminary Experiment in a Smart Home

Abstract: This paper presents a system to recognize distress speech in the home of seniors to provide reassurance and assistance. The system is aiming at being integrated into a larger system for Ambient Assisted Living (AAL) using only one microphone with a fix position in a non-intimate room. The paper presents the details of the automatic speech recognition system which must work under distant speech condition and with expressive speech. Moreover, privacy is ensured by running the decoding on-site and not on a remote… Show more

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Cited by 4 publications
(4 citation statements)
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“…Kaldi is an open-source state-of-the-art ASR system with a high number of tools and a strong support from the community. This new built system (Vacher et al, 2015b) was evaluated off-line using Cirdo-set in the same manner as in section 4. The challenge was to determine whether the use of SGMM-based acoustic models would improve performance through adaptation to the environment and users.…”
Section: Analysis Of the Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Kaldi is an open-source state-of-the-art ASR system with a high number of tools and a strong support from the community. This new built system (Vacher et al, 2015b) was evaluated off-line using Cirdo-set in the same manner as in section 4. The challenge was to determine whether the use of SGMM-based acoustic models would improve performance through adaptation to the environment and users.…”
Section: Analysis Of the Resultsmentioning
confidence: 99%
“…SGMMs were chosen over Deep Neural Network (DNN) models because SGMM is more adapted to situation where a low amount of adaptation data is available (Badenhorst and de Wet, 2017). This section is an extension of our previous work in (Vacher et al, 2015b).…”
Section: Analysis Of the Resultsmentioning
confidence: 99%
“…La figure 3.2 (à gauche) illustre la grande diversité de type de données à traiter pour réaliser cette inférence : continues (son), booléennes (état d'un périphérique), évènementielles (capteur de présence infrarouge PIR) ou discontinues (mesure de température à chaque changement). Ce système est articulé autour du séquenceur PATSH [74] illustré 3.2 (à droite) qui a permis l'intégration et l'échange de données en temps réel entre les différents modules (acquisition sonore, différenciation son/parole, Reconnaissance Automatique de la Parole ou RAP, contrôleur intelligent) et leur interaction avec le réseau domotique. L'étage Acquisition/Détection limite la durée du signal détecté 𝑠(𝑡) à 4,1 s et ne décide de la fin du signal qu'après une période de 0,25 s de silence.…”
Section: Vers Une Commande Vocale De La Domotique Sensible Au Contexteunclassified
“…13 The mapping relationship between gestures and control commands is stored in its database, and a classifier is trained by the random forest method to recognize the gesture information perceived by the depth imaging sensor. Vacher et al 14 proposed a voice-based home control system that uses deep learning methods to recognize user speech. Mofrad and Mosse 15 presented a method to separate and recognize speech in multi-user, multi-speech contexts.…”
Section: Related Workmentioning
confidence: 99%