\begin{table}[t]
\centering
\begin{tabular}{l}
\textbf{English} \\
I would like a mint tea \\
A tea and a coffee please \\
Do you have a table for four people \\
Could I reserve a table for seven thirty \\
Do you accept credit cards \\
\\
\textbf{French} \\
Puis-je avoir une bi\`ere \\
(Could I have a beer) \\
J’aimerais du fromage rape \\
(I would like some grated cheese) \\
Je voudrais une table dans le coin \\
(I would like a table in the corner) \\
Est-ce que je pourrais voir le menu \\
(Could I see the menu) \\
\\
\textbf{Japanese} \\
Biiru nihai onegai shi masu \\
(I would like two beers) \\
Madogawa no seki wa arimasu ka \\
(Is there a table by the window) \\
Betsubetsu ni haraemasu ka \\
(Can we pay separately) \\
Hachi ji han kara futari no teeburu wo yoyaku shitai no desu ga \\
(I would like to reserve a table for two people for half past eight)
\end{tabular}
\caption{Examples of CALL-SLT coverage in the restaurant domain, for English, French and Japanese.}
\label{table_1}
\end{table}

\begin{figure}[t]
\centering
\includegraphics{figure_1.pdf}
\caption{Mobile version of the CALL-SLT system, running on a Nokia tablet and using the graphical interlingua. The pictorial string is the graphical prompt, which here represents ``Ask politely for soup''. The buttons on the right are, from top to bottom, ``recognise'', ``next prompt'' and ``help''.}
\label{figure_1}
\end{figure}

plication. In particular, the grammar-based approach to recognition gives a response profile with accurate recognition on in-grammar utterances and poor or no recognition on out-of-grammar utterances, automatically giving the student feedback on the correctness of their language usage. Also, the platform’s rapid development facilities, based on semi-automatic specialisation of general resource grammars, have made it easy to create good speech recognisers for our initial domain (a tourist restaurant scenario), despite the very limited availability of training data. Although the recognisers for the various L2 languages are all built from development corpora of at most a few hundred examples, native speakers typically get per-sentence semantic error rates of under 10\%.

Two other differences between CALL-SLT and the MIT system are also worth highlighting. First, one of the main weaknesses of Wang’s and Seneff’s work is that prompts are in the student’s own language (the L1). This has the undesirable effect of tying the language being studied (the L2) too closely to the L1 in the student’s mind, and is quite contrary to mainstream theories of language acquisition. Instead of prompting student with sentences in the L1, our system shows them interlingua representations; these are created using semantic grammars based on our previous work on human-readable representations of interlingua \cite{11}.

Second, instead of focussing on a single language pair, we think of the problem more broadly as an activity in the multilingual language learning community. We structure learning activities so as to encourage students to contribute data both in the L1 and in the L2. Each student’s recorded native speaker data is used as a resource to help other students studying that language.

In more detail, the game that forms the basis of CALL-SLT is as follows. The system is loaded with a set of possible prompts, created by translating the development corpus into the interlingua. Each turn starts with the student asking for the next prompt. The system responds by showing her a surface representation of the underlying interlingua for the sentence they are supposed to produce in the L2. This representation can either be textual or pictorial. For example, a student whose L1 is French and whose L2 is English might be given the textual prompt

COMMANDER\_DE\_MANIERE\_POLIE SOUPE

or the graphical prompt shown in Figure~\ref{figure_1}. In both cases, an appropriate response would be something like ``Could I have some soup?'', ``I would like some soup'', or simply ``Soup, please''; the grammar supports most of the normal ways to formulate this type of request.

The student decides what she is going to say, presses the ``recognise'' button, and speaks. The system performs speech recognition using a Nuance 8.5 recognition package\footnote{We are currently porting the system so that it also runs under Nuance 9.0. Despite the similarity of names, Nuance 8.5 and Nuance 9.0 are very different, deriving from separate codebases.} compiled from a grammar-based language model, translates the result into the interlingua, matches it against the underlying interlingua representation of the prompt, gives the student feedback on the match, and adjusts the level of difficulty up or down. If the match was successful, the student’s recorded speech is also saved for future use.

The student may ask for help at any time. The system can give help either in speech or text form. Text help examples are taken from the original corpus, and can also be produced by translating from the interlingua back into the L1; speech help examples are created by recording successful interactions, or by doing TTS on text examples.

\section{Constructing language courses in CALL-SLT}

A frequent comment from early users of CALL-SLT has been that the system would be more useful if it included structured language exercises. Over the last few months, we have extended