Home monitoring for frailty detection through sound and speaker diarization analysis

As the French, European and worldwide populations are aging, there is a strong interest for new systems that guarantee a reliable and privacy preserving home monitoring for frailty prevention. This work is a part of a global environmental audio analysis system which aims to help identification of Activities of Daily Life (ADL) through human and everyday life sounds recognition, speech presence and number of speakers detection. The focus is made on the number of speakers detection. In this article, we present how recent advances in sound processing and speaker diarization can improve the existing embedded systems. We study the performances of two new methods and discuss the benefits of DNN based approaches which improve performances by about 100%.Comment: JETSAN, Jun 2023, Aubervilliers & Paris, Franc

Diarisation multimodale : vers des modèles robustes et justes en contexte réel

Speaker diarization, or the task of automatically determining "who spoke, when?" in an audio or video recording, is one of the pillars of modern conversation analysis systems. On television, the content broadcasted is very diverse and covers about every type of conversation, from calm discussions between two people to impassioned debates and wartime interviews. The archiving and indexing of this content, carried out by the Newsbridge company, requires robust and fair processing methods. In this work, we present two new methods for improving systems' robustness via fusion approaches. The first method focuses on voice activity detection, a necessary pre-processing step for every diarization system. The second is a multimodal approach that takes advantage of the latest advances in natural language processing. We also show that recent advances in diarization systems make the use of speaker diarization realistic, even in critical sectors such as the analysis of large audiovisual archives or the home care of the elderly. Finally, this work shows a new method for evaluating the algorithmic fairness of speaker diarization, with the objective to make its use more responsible.La diarisation du locuteur, c'est à dire la tache de déterminer automatiquement « qui parle, quand ? » dans un enregistrement audio ou vidéo, est un des piliers des systèmes modernes d'analyse des conversations. A la télévision, les contenus diffusés sont divers et couvrent à peu près tous les types de conversations, de la discussion calme entre deux personnes, aux débats passionnés, en passant par les interviews en terrain de guerre. L'analyse de ces contenus, réalisée par la société Newsbridge, requiert, en vue de leur archivage et de leur indexation, des méthodes de traitement robustes et justes. Dans ce travail, nous présentons deux nouvelles méthodes permettant d'améliorer la robustesse des systèmes via des approches de fusion. La première se concentre sur la détection d'activité vocale, prétraitement nécessaire à tout système de diarisation. La seconde est une approche multimodale qui tire notamment parti des dernières avancées en traitement du langage naturel. Nous voyons également que les récentes avancées des systèmes de diarisation rendent l'utilisation de la diarisation du locuteur réaliste y compris dans des secteurs critiques tels que l'analyse de larges archives audiovisuelles ou le maintien à domicile de personnes âgées. Enfin ce travail présente une nouvelle méthode d'évaluation de la justesse algorithmique de la diarisation du locuteur en vue de rendre son utilisation plus responsable

Privacy preserving personal assistant with on-device diarization and spoken dialogue system for home and beyond

International audienceIn the age of personal voice assistants, the question of privacy arises. These digital companions often lack memory of past interactions, while relying heavily on the internet for speech processing, raising privacy concerns. Modern smartphones now enable on-device speech processing, making cloud-based solutions unnecessary. Personal assistants for the elderly should excel at memory recall, especially in medical examinations. The e-ViTA project developed a versatile conversational application with local processing and speaker recognition. This paper highlights the importance of speaker diarization enriched with sensor data fusion for contextualized conversation preservation. The use cases applied to the e-VITA project have shown that truly personalized dialogue is pivotal for individual voice assistants. Secure local processing and sensor data fusion ensure virtual companions meet individual user needs without compromising privacy or data security

The role of CNVs in the etiology of rare autosomal recessive disorders: the example of TRAPPC9-associated intellectual disability

IF 3.636 (2017)International audienceIntroductionA large number of genes involved in autosomal recessive forms of intellectual disability (ID) were identified over the past few years through whole-exome sequencing (WES) or whole-genome sequencing in consanguineous families. Disease-associated variants in TRAPPC9 were reported in eight multiplex consanguineous sibships from different ethnic backgrounds, and led to the delineation of the phenotype. Affected patients have microcephaly, obesity, normal motor development, severe ID, and language impairment and brain anomalies.PatientsWe report six new patients recruited through a national collaborative network.ResultsIn the two patients heterozygous for a copy-number variation (CNV), the phenotype was clinically relevant with regard to the literature, which prompted to sequence the second allele, leading to identification of disease-associated variants in both. The third patient was homozygote for an intragenic TRAPPC9 CNV. The phenotype of the patients reported was concordant with the literature. Recent reports emphasized the role of CNVs in the etiology of rare recessive disorders.ConclusionThis study demonstrates that CNVs significantly contribute to the mutational spectrum of TRAPPC9 gene, and also confirms the interest of combining WES with CNV analysis to provide a molecular diagnosis to patients with rare Mendelian disorders