Interaction between Academia and Microsoft in Speech and Language Systems Kentaro Toyama Microsoft Research Chandar Sundaram, Andy Abbar, Alex Acero, Mythreyee Ganapathy, Raveesh Gupta SPLASH 2004

Interaction between Academia and Microsoft in Speech and Language Systems Kentaro Toyama Microsoft Research Chandar Sundaram, Andy Abbar, Alex Acero, Mythreyee Ganapathy, Raveesh Gupta SPLASH 2004

Academia and Microsoft Points of interaction for speech & language: Academic Developer Program MSDN AA -- Speech SDK MSDN AA -- Speech SDK Future Activities Future ActivitiesLocalization Local Language Program Local Language Program Microsoft Research Natural Language Processing Natural Language Processing Speech Speech University Relations University Relations

Academic Developer Program

MSDN Academic Alliance Subscription to MSDN valid for entire department Curriculum Tools Over 800 hours of curriculum materials Over 800 hours of curriculum materials Submit curricula you’ve developed Submit curricula you’ve developed Speech SDK bundled with MSDN

Future Offerings Visual Studio 2005 Express Edition “The Spoke” MSDN Academic Sessions Imagine Cup 2005 Project Portal

Localization

Localization Challenges:Time ~2 languages per year ~2 languages per yearComplexity People: linguists, computer users, developers, political scientists, translators, regional experts, etc. People: linguists, computer users, developers, political scientists, translators, regional experts, etc. Technology: keyboard drivers, character set standardization, fonts, currency symbols, glossary, translation of help files, etc. Technology: keyboard drivers, character set standardization, fonts, currency symbols, glossary, translation of help files, etc.Cost Business case not always present Business case not always present Customer involvement Microsoft should not unilaterally determine computer terminology Microsoft should not unilaterally determine computer terminology Click Computer

Local Language Program The Local Language Program: ~40 languages per year Localizes UI for Windows and Office In India: Hindi is done Hindi is done Telegu, Tamil, Kannada, Gujarati on its way Telegu, Tamil, Kannada, Gujarati on its way Resulting glossary, Language Interface Pack available to public at no cost Involvement by governments and universities critical!

Microsoft Research

Founded in 1991 Staff of over 650 in over 50 areas Internationally recognized research teams 5 lab locations around the world Research groups in Natural Language Processing Natural Language Processing Speech Speech Machine Learning Machine Learning Search Search Separate University Relations group India: Mythreyee Ganapathy India: Mythreyee Ganapathy

Impact on Product Text-to-speech engine (Windows) Command and Control (Windows) Smart Tags (Office) Grammar Checker (Office) IntelliShrink text compression (Office) Dictation (Office) Mandarin Chinese data entry (Office) Spam filter (MSN/Exchange) Speech API (SAPI) Speech Server

Research Philosophy University organizational model Flat structure, critical mass groups Flat structure, critical mass groups Open research environment Publications strongly encouraged Publications strongly encouraged Conference attendance high Conference attendance high Daily lectures by visiting researchers Daily lectures by visiting researchers Support for university research Nearly 15% of basic research budget directly invested in universities Nearly 15% of basic research budget directly invested in universities Lab grants, research grants, fellowships, etc. Internships for students

Text to Speech with Prosody MSR Asia’s Mulan project “The Speech Group in Microsoft Research Asia is conducting research in voice technology, such as speech recognition, speech synthesis, and speech-enabled information search.” “The Speech Group in Microsoft Research Asia is conducting research in voice technology, such as speech recognition, speech synthesis, and speech-enabled information search.”

Source Separation + + h 11 [n] h 22 [n] h 12 [n] h 21 [n] z1[n]z1[n] z2[n]z2[n] y1[n]y1[n] y2[n]y2[n] + + h 11 [n] h 22 [n] h 12 [n] h 21 [n] z1[n]z1[n] z2[n]z2[n] y1[n]y1[n] y2[n]y2[n] Idea: Estimate filters h 11 [n] and h 12 [n] that maximize p(z 1 [n]|  ) where is a HMM. Approximate HMM by a Gaussian Mixture Model with LPC parameters => EM algorithm with a linear set of equations

Multimodal Map

University Relations Liaison to universities Emphasis on curriculum and research Periodic workshops Faculty Summit in Redmond (July) India UR manager Mythreyee Ganapathy Mythreyee Ganapathy

Thank you!