%% Description of the design of our system

%% Things to point out: [mentioned above!]
%% Dispatcher hears Spanish, reads English MT
%% 	-- Two different modalities may be helpful?
%% 	-- [in previous systems, there has been a tension between the
%% 	naturalness of hearing the other speaker's actual speech and
%% 	the confusion that can cause with the synthesized translation]


\subsection{System architecture}

Based on the 9-1-1 dispatching domain characteristics described in the
preceding section, we are proposing a highly assymetrical overall system
design.

In the {\bf dispatcher-to-caller} direction, we will build a
(relatively) simple phrase-based text-to-speech translation system.
This will require no English automatic speech recognition (ASR), no
(or very simple) 
true English-to-Spanish machine translation (MT), and relatively simple
phrase-based Spanish speech synthesis.

The caller-to-dispatcher direction is more interesting.

This is the overall design of our 9-1-1 system 
\begin{itemize}
%% No need for English ASR!
\item      Build $E->S$ domain phraselator ``MT''

\item      Build full Spanish ASR, domain-adapted
		%% [with dialect id/adaptation??]
		%% [with level-of-distress id/adaptation??]
		%% [with multiple sub-domains based on 9-1-1 tree??]
\item      Build $S->E$ ``real'' translator
	    How to leverage decision tree?? [see above]
%% No need for English TTS!
\item      Build (domain-limited) Spanish TTS [easy]

\end{itemize}

[be sure to mention dispatcher's decision tree]


No synthesis research at all: 
Spanish pre-recorded, plus simple domain synthesizer.

SR: emotional/stressed speech is hard, but we have access to the data
of actual calls [see letter].
Telephone-based speech
	[Janus has done SwitchBoard Eval]


MT research


\subsection{Project workplan}

Good to include what happens in which years... [no quarterlies though].
Mention what students and UG will do.

\begin{itemize}
\item Acquire 9-1-1 decision tree, recordings of actual 9-1-1 calls
	%% [descr. of available data, sample; letter of cooperation]

\item Transcribe recordings, US English and Spanish (several dialects)
	%% [some calls are three-way with interpreter]
	%% [If not doing English ASR/TTS, easier English transcription?]
%% Data transcription:
%%      capture dialect, intoxication, emotion: "enriched transcription"

\item Decide specific $S->E$ MT approach based on transcripts, 9-1-1 tree.
	Probably either EBMT or SMT-variant; internal existing tech base for both
	Combined with simple limited-domain high-Q engine??
	Classifier idea?  [Similar to Knight's Babylon approach]

\item Build initial pilot version of system

\item Evaluate pilot version with CCPD

\item Update system from test

\item Evaluate alpha version, possibly with CCPD
\end{itemize}

%%\section{Evaluation Plan}

%% 	   Little evaluation in real situations (Tongues; Nespole; any others?)
%% "We don't foresee it being necessary, but we'll get IRB approval if it becomes appropriate."

Mention our experience in Tongues, Nespole! evaluations.
Refer to Babylon future evaluation somewhere.

We'll evaluate system in real environment.
But no live use yet:
   Test with real data off-line;
   Experts role-playing.

%% "We don't foresee it being necessary, but we'll get IRB approval if it becomes appropriate."

\subsection{Unfunded Collaborator}
Describe, and refer to letter in Supplemental Documentation


\subsection{Broader Impacts}
Actually, fits best at end of section 2??

%%\section{Education and Outreach}

[include this with social benefits: ``In addition, the project will
support two doctoral students, ... teaching, publishing'']
-- advance the state of fundamental research in speech recognition and
machine translation

%%\section{Addressing Social Goals(??)}

Should be an ``integral part of the narrative''...

Social aspects: Chronic low-level need, plus Natural disasters and
Homeland Security (low key) [already mentioned in domain description]

%%\section{Management Plan (only needed for ITRs??)}

We will now describe in turn our approaches to the Speech Recognition,
(very limited) Speech Synthesis, and Machine Translation component
technologies in our proposed system.
