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Seite 723
Linear prediction of speech . By J. D. MARKEL and A. H. GRAY JR . Heidelberg : Springer Verlag , 1976. Pp . xii , 288. $ 29.80 . Reviewed by EDWARD C. CARTERETTE , UCLA A filter works on an input stream and produces an altered output ...
Linear prediction of speech . By J. D. MARKEL and A. H. GRAY JR . Heidelberg : Springer Verlag , 1976. Pp . xii , 288. $ 29.80 . Reviewed by EDWARD C. CARTERETTE , UCLA A filter works on an input stream and produces an altered output ...
Seite 724
If one wishes to predict a particular sample s ( n ) , a natural way is to find a predictor ŝ ( n ) by taking a linear combination of the prior M samples . The prediction error would be e ( n ) s ( n ) – ŝ ( n ) M 1 = 1 with ŝ ( n ) as ...
If one wishes to predict a particular sample s ( n ) , a natural way is to find a predictor ŝ ( n ) by taking a linear combination of the prior M samples . The prediction error would be e ( n ) s ( n ) – ŝ ( n ) M 1 = 1 with ŝ ( n ) as ...
Seite 785
... of R. Lakoff 1972 ( in the context of a generative - semantics - biased discussion ) that will is not a future - tense marker , but a modal which actually softens the force of a ' certain prediction ' like John leaves tomorrow .
... of R. Lakoff 1972 ( in the context of a generative - semantics - biased discussion ) that will is not a future - tense marker , but a modal which actually softens the force of a ' certain prediction ' like John leaves tomorrow .
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Inhalt
Another glance at main clause phenomena Dwighi Bolinger | 511 |
Amount relatives Greg N Carlson | 520 |
Where do cleft sentences come from ? Jeannette K Gundel | 543 |
Urheberrecht | |
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acceptable analysis appear apply argument assume assumptions auxiliary believe Chapter Chomsky claim clause complement compounds considered constructions contains context course deletion derived determiner discussion distinction elements English evidence example existence expression fact FIGURE formal French function give given grammar important indicate interesting interpretation involved John language least lexical linguistic meaning mention Michigan modals nature noted noun object occur particular passive phonological position possible prediction present Press principle probability problem properties proposed question Raising reading reason reference relations relationship relative require result rules seems semantic sensei sentences significance similar single speakers specific speech stress structure suggests surface symbols syntactic syntax tense theory tion transformational underlying University verbs vowels