An Interactive and Efficient Voice Processing For Home Automation System

Home networking has evolved from linked personal computers to a more complex system that encompasses advanced security and automation applications. Once just reserved for high-end luxury homes, home networks are now a regular feature in residences. These networks allow users to consolidate heating, air conditioning, lighting, appliances, entertainment, intercom, telecommunication, surveillance and security systems into an easy-to-operate unified network. Interactive applications operated by voice recognition, for example integrated door security systems and the ability to control home appliances, are key features of home automation networks. This interactive capability depends on high-quality voice processing technology, including acoustic echo cancellation, low signal distortion and noise reduction techniques. A home automation system must also be scalable to allow future evolution, flexible to support field upgrades, interactive, easy-to-use, costefficient and reliable. This article introduces some of the voice quality performance issues and design challenges unique to home automation systems. It will discuss home automation network applications that rely on voice processing, and examine some of the critical features and functionality that can help ease design complexity and cost to deliver enhanced performance

Studying the “Wisdom of Crowds” at Scale

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been dubbed the “wisdom of the crowd”. However, due to the varying contexts, sample sizes, methodologies, and scope of previous studies, it has been difficult to gauge the extent to which conclusions generalize. To investigate this question, we carried out a large online experiment to systematically evaluate crowd performance on 1,000 questions across 50 topical domains. We further tested the effect of different types of social influence on crowd performance. For example, in one condition, participants could see the cumulative crowd answer before providing their own. In total, we collected more than 500,000 responses from nearly 2,000 participants. We have three main results. First, averaged across all questions, we find that the crowd indeed performs better than the average individual in the crowd—but we also find substantial heterogeneity in performance across questions. Second, we find that crowd performance is generally more consistent than that of individuals; as a result, the crowd does considerably better than individuals when performance is computed on a full set of questions within a domain. Finally, we find that social influence can, in some instances, lead to herding, decreasing crowd performance. Our findings illustrate some of the subtleties of the wisdom-of-crowds phenomenon, and provide insights for the design of social recommendation platforms