Affiliation: OHSU Center for Spoken Language Understanding
Funding Period: 2007 - 2010
Funding Source: ORCATECH Roybal Pilot Grant
View related topics
Abstract
Health, quality of life and treatment outcomes in older adults have been shown to be influenced by their level of social engagement in personal relationships and activities — both positive or negative – with family members, peers, community members, local institutions, and, at the broadest level, society. Because of the heavy reliance on the cognitive and memory function of the subject, current measures of social engagement, which are based on self-report, suffer from inaccuracies. Further, they do not provide fine-level information necessary to design intervention and treatments. While advances in sensor technology is being exploited to augment self-report based assessment of physical abilities of older adults, such advances have not been realized in the assessment of social engagement due to inherent difficulties in characterizing an individual’s network of social support. This network is multi-faceted and is mediated through several different types of communications, including emails, financial transactions, and conversations with a wide range of persons, including family members, friends, medical personnel, and business associates. Of these types of communication, adult humans rely on conversations for most social interactions. Using conversations as source of data reflecting social engagement, advanced speech and language technology now gives us the capability to characterize these interactions.
Our long-term goal is to design a computational framework to measure social engagement that accounts for variations in size, type and nature of an individual’s social network using conversations as our data source.
Our research objective in this proposal is to create algorithms that detect spoken language markers in an older adult’s everyday conversations that are indicative of an individual’s social engagement. The three specific aims of this proposal are:
- to determine the feasibility and acceptability of collecting conversational speech to assess social engagement of older adults,
- to design algorithms to detect spoken-language markers of social engagement from conversations, and
- to identify the spoken-language markers of social engagement.