The OpenVXI is a portable open source based toolkit that interprets the VoiceXML dialog markup language. It is designed to serve as a framework for system integrators and platform vendors who want to incorporate VoiceXML into their platform. A first version of the toolkit was released in the winter of 2001, with a second version released in September of 2001. A number of companies and individuals have adopted the toolkit for their platforms. In this paper we discuss the architecture of the toolkit, the architectural issues involved with implementing a framework for VoiceXML, performance results with the OpenVXI, and future directions for the toolkit.

We develop an infrastructure for managing, indexing and serving multimedia content in digital libraries. This infrastructure follows the model of the Web, and thereby is distributed in nature. We discuss the design of the Librarian, the component that manages meta data about the content. The management of meta data has been separated from the media servers that manage the content itself. Also, the extraction of the meta data is largely independent of the Librarian. We introduce our extensible data model and the daemon paradigm that are the core pieces of this architecture. We evaluate our initial implementation using a relational database. We conclude with a discussion of the lessons we learned in building this system, and proposals for improving the flexibility, reliability, and performance of the system.

A New Algorithm for Robust Sp eech Recognition: The Delta VectorTaylor Series ApproachPedroJ.Moreno and Brian Ebermanemail: [email protected], [email protected] Equipment Corp orationCambridge Research Lab oratoryABSTRACTIn this pap er we present a new mo del-based comp ensationtechnique called Delta Vector Taylor Series (DVTS). Thisnew technique is an extension and improvementoer theVector Taylor Series (VTS) approach [7] that addressesseveral of its limitations .In particular, we presentanew statistical representation for the distribution of cleansp eech feature vectors based on a weighted vector co de-b o ok. This change to the underlying probabili ty densityfunction (PDF) allows us to pro duce more accurate andstable solutions for our algorithm. The algorithm is alsopresented in a EM-MAP framework where some the en-vironmental parameters are treated as random variableswith known PDF's. Finally,we explore a new comp ensa-tion approach based on the use of convex hulls.Weevaluate our algorithm in a phonetic classi cati on taskon the TIMIT [5] database and also in a small vo cabu-lary size sp eech recognition database. In b oth databasesarti cial and natural noise is injected at several signal tonoise ratios (SNR). The algorithm achieves matched p er-formance at all SNR's ab ove 10 dB.1.Intro ductionOver the last years several techniques have b een prop osedto deal with the problem of sp eech recognition in noisy en-vironments. Some of them such as PMC [3], or MLLR [6]have used the recognition engine and its rich statisticalrepresentation (more than 90,000 Gaussians in systemslike SPHINX-3 and HTK [9]) to mo del and comp ensatefor the e ects of the environment on sp eech recognitionsystems. Other techniques like CDCN [1] and POF [8]among others have used a reduced set of Gaussian mix-tures (typically 256 or less) to mo del the sp eechfeature vectors and prepro cess the noisy sp eech featuresvectors to e ectively clean the features b efore b eing pro-cessed by the recognition engine.The use of a rich statistical representation improves p er-formance, but has the drawback of using the whole sp eechrecognition engine with its asso ciated complexity.Anideal robust recognition technique should have the advan-tages of a rich statistical representation and at the sametime b eing simple and fast in its op eration.The Delta Vector Taylor Series (DVTS) approachis anattempt in this direction.It tries to gain the b ene tsof a rich statistical representation and a low complexitytechnique for robust sp eech recognition. It tries to achievethese goals by using a di erent statistical representationfor the sp eech feature vectors.The outline of the pap er is as follows. In section 2 wedescrib e the DVTS algorithm.In section 3 we brieydescrib e the necessary mo di cations to the algorithm tomakeit work as a lter.In section 4 we describ e ourexp erimental results and nally in section 5 we presentour conclusions.2.New Algorithm: Delta-VTSDVTS mo dels the sp eech feature vectors as a weightedsum of multidimensi onal Dirac deltasp(x)=M1Xk=0P[k])(1)where eachvector function(xk) is mo deled as(xk)=D1Yi=0ii;k)(2)P[k]is ana prioriprobability of observing a particulardelta. The sum of these probabili ties must add up to one.This novel representation of the PDF ofxhas several ad-vantages. First of all it greatly simpli es the mathemat-ical assumptions of the VTS [7] algorithm. It pro ducesa simple, fast, robust and direct formulation of the EMsolutions already presented in [7].In this pap er we assume a mo del of the environmentinwhich sp eech is corrupted by unknown additive stationarynoise and unknown linear lteringZ(!)=X)jH2+N(3)whereZ(!) represents the p ower sp ectrum of the de-graded sp eech,X(!) is the p ower sp ectrum of the cleansp eech,jH(!)2is the transfer function of the linear lter,andN(!) is the p ower sp ectrum of the additive noise.In the log-mel-sp ectral domain this can b e expressed asz=x+ log (exp (q) + exp (n))(4)or in more general termsz=x+f(;nq)(5)

An approach to manipulation that uses all the available manipulation surfaces of the robot to act on and sense the environment is outlined. The kinematic, mechanism, actuation, and control implications of such a design are discussed and initial experimentation with a prototype mechanism are described. A three-degree-of-freedom underwater manipulator using a number of the resulting design concepts is described.>