In this paper, we describe several approaches to language-independent spoken term detection and compare their performance n a common task, namely “Spoken Web Search”. The goal of this part of the MediaEval initiative is to perform low-resource anguage-independent audio search using audio as input. The data was taken from “spoken web” material collected over mobile hone connections by IBM India ...

This paper describes a method for designing robots to learn self-motivated behaviors rather than externally specified behaviors. Self-motivation is viewed as an emergent property arising from two competing pressures: the need to accurately predict the environment while simultaneously wanting to seek out novelty in the environment. The robot’s internal prediction error is used to generate a rein...

In this paper we investigate protocols for scheduling applications that consist of large numbers of identical, independent tasks on large-scale computing platforms. By imposing a tree structure on an overlay network of computing nodes, our previous work showed that it is possible to compute the schedule which leads to the optimal steady-state task completion rate. However, implementing this opt...

We present an approach to speaker recognition in the textindependent domain of conversational telephone speech using a text-constrained system designed to employ select highfrequency keywords in the speech stream. The system uses speaker word models generated via Hidden Markov Models (HMMs) — a departure from the traditional Gaussian Mixture Model (GMM) approach dominant in text-independent wor...

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across diffe...

A scheme for allowing a problem-solver to improve its performance with experience is outlined. A more complete definition of the scheme for a particular problem-solving program is given. Some results showing the effectiveness of the scheme are reported.

In this work, we investigate the use of RASTA filter in the TANDEM feature extraction method when trained with a task independent data. RASTA filter removes the linear distortion introduced by the communication channel which is demonstrated in a 18% relative improvement on the Numbers 95 task. Also, studies yielded a relative improvement of 35% over the basic PLP features by combining TANDEM fe...

This paper presents the underlying algorithms of an emotion model within a task-independent framework. This model, called NEMO is a taskindependent model that integrates a module of needs for emotional expressions. We suggest that appraisals can be confined within various scopes of needs. In other words, the emotion framework allows control over appraisals based on a set of pre-defined levels o...