Compiling Grammar-based Speech Application ... - CiteSeerX

10 Introduction
is represented by a noun phrase in this concrete syntax. The mkNP function
is overloaded, and the version of it that is used in the linearization of pizza
takes two arguments, one of type Det (determiner, the linearization category
of Number) and one of type CN (common noun, here constructed from another
common noun and an adverbial phrase). In the linearization of cheese, a version
of mkNP is used that takes a determiner and a noun as arguments. The
linearization of ConsT opping, one of the two constructors in the [Topping] category,
uses a third version of mkNP that takes a conjunction and two noun
phrases as arguments.
Another noteworthy feature is that the linearization of large uses variants,
to allow alternative ways to express a given input. This is used extensively in
realistic dialogue system grammars, to handle variation in how input can be
expressed without complicating the semantics.
Figure 7 shows the German concrete syntax for the abstract syntax in Figure
5. Note that the only difference compared to the English concrete syntax in
Figure 6 is that it imports German resource grammar modules, and specifies the
German application-specific words. GF’s parameterized modules (Ranta 2007)
can be used to create a shared implementation of the language independent
part, but that has not been done here.
4 Paper I: Speech Recognition Grammar Compilation
in Grammatical Framework
Speech recognizers use speech recognition grammars (also known as language
models) to limit the input language in order to achieve acceptable recognition
performance. In the paper “Speech Recognition Grammar Compilation in
Grammatical Framework”, we show how speech recognition grammars in several
commonly used context-free and finite-state formalisms can be generated
from GF grammars. We also describe generation of semantic interpretation code
which can be embedded in speech recognition grammars.
4.1 An Example
For the Input category in the example grammar in Figure 5 and Figure 6, we
can generate the finite-state model shown in Figure 8. Finite-state models such
as this one are used to guide the HTK speech recognizer.
4.2 Contribution
I wrote the paper myself, and I implemented the various grammar translations
it describes.