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1	Speech Technology in Language Learning Applications InterSpeech Pittsburgh, Pennsylvania September 2006 Jared Bernstein Ordinate Corporation/Harcourt Assessment
2	What I’ve Learned So Far Data/Observation ‘Zinkgraf’ Model: Native like language skill can come from a focus from the beginning on no errors in pronunciation from the beginning; bottom-up Immersion accelerates fluency Lessons Learned Time-on-task dominates factors affecting performance Automaticity underlies most spoken communication (you need to know aspects of the language at the automatic level in order to have enough cognitive resources for quick analysis and response to new and complex daily situations
3	Pronunciation in Communication
4	Message Encoding in Communication
5	Simple Models Help Reading Comprehension = Decoding times Language Comprehension RC = D x LC L2 Spoken Language Proficiency Proficiency = Pronunciation X Vocabulary X (1 + everything else) Proficiency @ pronciation X vocabulary X (1 + syntax + rhetoric + illocution + sociology)
6	Student Ranges Compared
7	Time to Learn Helps
8	Listening/Speaking in Real Time Is quite challenging More often like ping-pong or dodge-ball Not usually like email or geography
9	Assessment Can Help