2. Spoken Dialog Systems Diane J. Litman Professor, Computer Science Department

3. 2 Spoken Dialog Systems Systems that interact with users via speech Provide automated telephone or microphone access to a back-end Advantages: naturalness, efficiency, eyes and hands free user Speech Recognition TTS or recording DB, web, system Spoken Dialog System

4. 3 Challenges in Spoken Dialog Systems Automated speech recognition  Sphinx, Microsoft Speech, Dragon Naturally Speaking Natural language understanding Dialog Management  How to keep the conversation going? Best strategy?  How to detect errors in communication?  How to recover from errors? Spoken language generation

5. 4 Application areas I have worked on AT&T  Phone-based Information Access  Call Centers  Social Networking Systems Pitt  (Physics) Tutoring  Backup for Port Authority human operators Other Interests  Training, Troublesheeting, PDA’s

6. 5 Speech-based Computer Tutors What are they? Example  Tutor: Well, if an object has non zero constant velocity, is it moving or staying still?  Student: Moving  Tutor: Yep. If it’s moving, then its position is changing. So then what will happen to the packet’s horizontal displacement from the point of its release?  Student: It will change Intersection of two fields:  Spoken Dialog Systems  Intelligent Tutoring Systems

7. 6 Intelligent Tutoring Systems Education  Classroom instruction [most frequent form]  Human (one-on-one) tutoring [most effective form] Computer tutors – Intelligent Tutoring Systems  Not as good as human tutors  Ways to address the performance gap (Spoken) dialog systems Affective (dialog) systems

8. 7 Back-end is Why2-Atlas system [VanLehn, Jordan, Rose et al. 2002] Sphinx2 speech recognition and Cepstral text-to-speech

9. 8 Back-end is Why2-Atlas system [VanLehn, Jordan, Rose et al. 2002] Sphinx2 speech recognition and Cepstral text-to-speech

10. 9 Back-end is Why2-Atlas system [VanLehn, Jordan, Rose et al. 2002] Sphinx2 speech recognition and Cepstral text-to-speech

11. 10 Current Research Directions Automatic System Optimization  Can a system learn to optimize behavior based on prior data? Evaluation  How can we tell if we are improving a system? Speech vs keyboard, TTS vs recordings, graphics vs. transcripts  Can systems be tested with simulated rather than real users? Affective Computing  How can user emotions be predicted in real-time?  How can the system exploit such information? Prosodic and Linguistic Analysis  Respond to both what a user says, and how it is said

12. Human-Computer Excerpt Tutor26: Let me ask my question in a more general way. What happens to the velocity of a body when there is no force acting on it? Student27:dammit (ASR: it is) Tutor28 :Could you please repeat that? Student29 :same (ASR: i same) Tutor30 :Right. And what happens to the HORIZONTAL velocity of a body when there is no HORIZONTAL force acting on it? Student31:zero (ASR: the zero) Tutor32 :Well... OK, let's consider how forces in the vertical direction affect the motion of a body in a horizontal direction in a different scenario Student33:oh gravity you already said this (ASR: beats gravity you're exert less) Tutor34:Fine. Are there any other forces acting on the apple as it falls? Student35:no why are you doing this again (ASR: no y and to it yes) Tutor36:Correct. The only force acting on the apple as it falls to the earth is the earth's gravitational force. In which direction does gravitational force act? Student37:downward you computer (ASR: downward you computer)

13. 12 Thank You! Questions? Further Information http://www.cs.pitt.edu/~litman/itspoke.html