SPEECH RECOGNITION LEXICON DAY 19 – OCT 9, 2013 Brain & Language LING NSCI Harry Howard Tulane University

Course organization The syllabus, these slides and my recordings are available at If you want to learn more about EEG and neurolinguistics, you are welcome to participate in my lab. This is also a good way to get started on an honor's thesis. The grades are posted to Blackboard. 10/09/13Brain & Language, Harry Howard, Tulane University 2

REVIEW 10/09/13Brain & Language, Harry Howard, Tulane University 3

Review MethodologySupports strong SMH? dichotic listeningyes, but morse code shows same response (p. 127) categorical perceptionno, because animals have same response duplex perceptionno, because animals have same response sine-wave speechyes MethodologyShows development of modularity Perceptual magnet effectyes, not found before 6 months Prosodic parsingyes, not found before 6-12 months Methodology Gating speech perception involves active feature extraction and structure building 10/09/13Brain & Language, Harry Howard, Tulane University 4

THE SPEECH RECOGNITION LEXICON Ingram §7 10/09/13Brain & Language, Harry Howard, Tulane University 5

Linguistic model, Fig. 2.1 p /09/13Brain & Language, Harry Howard, Tulane University 6 Discourse model Syntax Sentence prosody Morphology Word prosody Segmental phonology perception Segmental phonology perception Acoustic phonetics Feature extraction Segmental phonology production Segmental phonology production Articulatory phonetics Speech motor control INPUT Sentence level Word level

Storing on a hard disk How does a computer store files on its hard drive? By writing them in sequence or where ever there is space. 10/09/13Brain & Language, Harry Howard, Tulane University 7

Retrieving from a hard disk How does a computer find files on its hard drive (say, when you search for one by its name)? It searches for it in sequence or randomly. How long does it take? 10/09/13Brain & Language, Harry Howard, Tulane University 8

How would this work for lexical retrieval? Ingram’s example The phoneme detector department detects /k/. A comparator starts looking for all the files that begin with /k/, perhaps ordered in terms of frequency. The phoneme detector department detects /æ/. The comparator rejects the files that don’t begin with /kæ/ and starts searching the remaining files, perhaps ordered in terms of frequency. “It is an open bet whether the word cat would be retrieved before or after the detection of /t/.” (p. 143) Problems Other factors influence speed of retrieval, such as whether the target word has been seen recently. Adding such factors to a serial search model tends to make it slow down! 10/09/13Brain & Language, Harry Howard, Tulane University 9

An alternative: the TRACE II model 10/09/13Brain & Language, Harry Howard, Tulane University 10

Observations TRACE implements parallel computation, rather than serial or sequential computation. It is both bottom-up (driven by data) and top-down (driven by expectations). Bottom up The successive winnowing of a set of cohorts is modeled by decaying activation of competitors as more information is gathered. Top down Word frequency is modeled by lowering the threshold of activation of more frequent word units, so they need less activation. The phoneme restoration effect is modeled by the word units supplying the missing activation of a phoneme unit. [kæØ] can be heard as ‘cat’. The Ganong effect is modeled in the same way. [kæ ] can be heard as ‘Cass’ or ‘cash’ in the proper context. 10/09/13Brain & Language, Harry Howard, Tulane University 11

Simple recurrent networks Read what Ingram says to get the general idea of what it is supposed to do. 10/09/13Brain & Language, Harry Howard, Tulane University 12

Modeling variability We will go over it on Monday. 10/09/13Brain & Language, Harry Howard, Tulane University 13

NEXT TIME Q5. Finish Ingram §7 & start §8. ☞ Go over questions at end of chapter. 10/09/13Brain & Language, Harry Howard, Tulane University 14