The various CARTs used in Festival

[Eoin M]

Can details about the questions/features used for the CART algorithm at various stages of the Festival pipeline be accessed easily through Festival?

On what training data were these decision trees trained?

[Simon]

CARTs are used in several places within Festival. The best example is the letter-to-sound model. Look at the file lib/dicts/cmu/cmu_lts_rules.scm in http://www.cstr.ed.ac.uk/downloads/festival/2.4/festlex_CMU.tar.gz which is a letter-to-sound classification tree trained on the CMU lexicon.

Here’s the start of the tree for the letter “a” from that file:

(set! cmu_lts_rules '(
(a
 ((n.name is r)
  ((p.name is e)
   ((n.n.name is t)
    ((p.p.name is h)
     (((aa0 0.030303) (aa1 0.969697) aa1))
....etc

n.name refers to the predictor “name of the next letter” and the line

(((aa0 0.030303) (aa1 0.969697) aa1))

is a leaf, showing the distribution of values for the predictee.

The letter-to-sound CART is trained on the pronunciation dictionary (which was written by hand). Others are trained on hand-labelled data of other types (e.g., speech with hand-annotated phrase breaks).

CARTs can also be written by hand. One reason for doing this is when no training data are available. Here’s an example of a CART for predicting phrase breaks from punctuation.

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