Beyond the Equal Error Rate - About the Inter-relationship Between Algorithm and Application

Speaker verification technologies have many commercial applications, such as direct banking, cellular transactions, credit card operations and E-Commerce. Voice based verification can answer the need for a secure, friendly and cost effective authentication tool required by the finance, commerce and Telecommunication markets. Introducing an operational large-scale system to the market requires much more than a good algorithm. Several design issues should be considered, such as: How to retrieve the audio from the telephony network? What is the optimal way to store and maintain the voice signatures? How to receive claimed identity? Is log likelihood a meaningful score? The development process opens a wide range of subjects for algorithmic research. Among them are: time evolution of speaker models, decision mechanisms, effective scoring, and new ways for constructing world models. Algorithmic research and system development cannot be done independently. Continuous joint work is necessary in order to have successful operational systems, which will make speaker verification the natural authentication means in remote services and transactions. The paper reviews the inter-relationship between algorithmic research and system development based on the experience from the speaker verification product of Persay Ltd. We describe the main problems during the system design process, and discuss the alternatives for solution. A list of research problems, derived from the implementation process is presented

The Effects of Attrition on the Growth and Equity of Competitive Services

The growth of a new service is similar to a leaking bucket: There is an influx of new customers and, concurrently, an outflow of customers who either switch to competitors or leave the category. This attrition is a major concern for service providers and significantly affects long-range profits. In this study, the authors investigate the influence of attrition on the growth of service markets. They develop a model of a multifirm growing market, where a firm may acquire customers from the pool of nonusers (which can include new customers as well as customers who disadopted the category in the past) and also acquire customers who switched from competitors. Alternatively, the firm may lose customers who switch or “churn” to a competitor or leave the category entirely. By capturing the complex dynamics of customer acquisition and retention, this model enables an in-depth analysis of the growth of services. The authors use the model to explore the influence of attrition on the service category and on a particular brand. For service categories, they show that ignoring attrition biases the diffusion parameters and hence affects management diagnostics. For the individual brand, they present a brand-level growth model and use it to capture the effect of attrition on the firm’s customer equity: they calculate the customer equity of a growing service and evaluate service firms that operate in competitive industries, including Amazon.com, Barnes&Noble.com, E*Trade, Mobistar, and SK Telecom. For four of the five firms, the results are close to the stock market valuations, which may indicate the role of customer equity in the valuation of growing service firms. The services growth model adds to the customer equity approach not only by explicitly incorporating customer attrition into market growth, but also by allowing for inter-firm churn dynamics to be included in the estimation. Hence, it is especially well suited to dealing with cases where interfirm customer churn is an integral part of the growth process

Mitigating Emergency Department Crowding With Stochastic Population Models

Environments such as shopping malls, airports, or hospital emergency departments often experience crowding, with many people simultaneously requesting service. Crowding is highly noisy, with sudden overcrowding "spikes". Past research has either focused on average behavior or used context-specific non-generalizable models. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding, using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) reduces severe overcrowding events by 50%. Such forecasting is crucial in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many natural systems.Comment: 21 pages, 6 figures + Supplementary informatio

Talk bursts: The role of spikes in prerelease word-of-mouth dynamics

Before their launch, many new products generate word of mouth (WOM) on social media. Such WOM typically increases toward the release date and contains sudden spikes. These spikes capture manifestations of peak consumer attention and are therefore of managerial importance, yet they have not received research attention. This article is the first to provide a comprehensive descriptive treatment of WOM spikes. The authors propose a conceptual framework to present spikes as a standalone WOM dimension and explain their emergence. They employ a robust filtering procedure to detect spikes and apply it in a data set of 90,000 prerelease online WOM messages on 157 Hollywood movies. The results indicate that prerelease spikes are widely prevalent: While some of them are event-driven, emerging in response to firm-created communications (e.g., trailer release), they are far more likely to emerge spontaneously. Content analysis reveals that WOM in spikes is more positive in sentiment and is more likely to deal with factual details than is WOM outside spikes. Prerelease WOM spikes also contribute significantly to the predictability of future product sales. Keywords bursts, content analysis, prerelease word of mouth, user-generated content, robust time series analysi

Talk bursts: The role of spikes in prerelease word-of-mouth dynamics

Before their launch, many new products generate word of mouth (WOM) on social media. Such WOM typically increases toward the release date and contains sudden spikes. These spikes capture manifestations of peak consumer attention and are therefore of managerial importance, yet they have not received research attention. This article is the first to provide a comprehensive descriptive treatment of WOM spikes. The authors propose a conceptual framework to present spikes as a standalone WOM dimension and explain their emergence. They employ a robust filtering procedure to detect spikes and apply it in a data set of 90,000 prerelease online WOM messages on 157 Hollywood movies. The results indicate that prerelease spikes are widely prevalent: While some of them are event-driven, emerging in response to firm-created communications (e.g., trailer release), they are far more likely to emerge spontaneously. Content analysis reveals that WOM in spikes is more positive in sentiment and is more likely to deal with factual details than is WOM outside spikes. Prerelease WOM spikes also contribute significantly to the predictability of future product sales.\u3cbr/\u3e\u3cbr/\u3eKeywords bursts, content analysis, prerelease word of mouth, user-generated content, robust time series analysi

Estimating emergency department crowding with stochastic population models.

Environments such as shopping malls, airports, or hospital emergency-departments often experience crowding, with many people simultaneously requesting service. Crowding highly fluctuates, with sudden overcrowding "spikes". Past research has either focused on average behavior, used context-specific models with a large number of parameters, or machine-learning models that are hard to interpret. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding. We test the model using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) cuts the probability of severe overcrowding events by 50%. Such forecasting is critical in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many systems in nature

Modelling the expected probability of correct assignment under uncertainty

When making important decisions such as choosing health insurance or a school, people are often uncertain what levels of attributes will suit their true preference. After choice, they might realize that their uncertainty resulted in a mismatch: choosing a sub-optimal alternative, while another available alternative better matches their needs. We study here the overall impact, from a central planner’s perspective, of decisions under such uncertainty. We use the representation of Voronoi tessellations to locate all individuals and alternatives in an attribute space. We provide an expression for the probability of correct match, and calculate, analytically and numerically, the average percentage of matches. We test dependence on the level of uncertainty and location. We find that the overall mismatch is considerable even for low uncertainty—a possible concern for policy makers. We further explore a commonly used practice—allocating service representatives to assist individuals’ decisions. We show that within a given budget and uncertainty level, the effective allocation is for individuals who are close to the boundary between several Voronoi cells, but are not right on the boundary.QRD/Kouwenhoven LabQuTec