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poster-template.tex
\documentclass[final,t]{beamer}
\mode<presentation>
{
% \usetheme{Warsaw}
% \usetheme{Aachen}
% \usetheme{Oldi6}
% \usetheme{I6td}
\usetheme{I6dv}
% \usetheme{I6pd}
% \usetheme{I6pd2}
}
% additional settings
\setbeamerfont{itemize}{size=\normalsize}
\setbeamerfont{itemize/enumerate body}{size=\normalsize}
\setbeamerfont{itemize/enumerate subbody}{size=\normalsize}
% additional packages
\usepackage{times}
\usepackage{amsmath,amsthm, amssymb, latexsym}
\usepackage{exscale}
%\boldmath
\usepackage{booktabs, array}
%\usepackage{rotating} %sideways environment
\usepackage[english]{babel}
\usepackage[latin1]{inputenc}
\usepackage[orientation=landscape,size=custom,width=200,height=120,scale=1.9]{beamerposter}
\listfiles
\graphicspath{{figures/}}
% Display a grid to help align images
%\beamertemplategridbackground[1cm]
\title{\huge Speech Recognition Techniques for a Sign Language Recognition System}
\author[Dreuw et al.]{Philippe Dreuw, David Rybach, Thomas Deselaers, Morteza Zahedi, and Hermann Ney}
\institute[RWTH Aachen University]{Human Language Technology and Pattern Recognition, RWTH Aachen University, Aachen, Germany}
\date[Aug. 31 , 2007]{Aug. 31 , 2007}
% abbreviations
\usepackage{xspace}
\makeatletter
\DeclareRobustCommand\onedot{\futurelet\@let@token\@onedot}
\def\@onedot{\ifx\@let@token.\else.\null\fi\xspace}
\def\eg{{e.g}\onedot} \def\Eg{{E.g}\onedot}
\def\ie{{i.e}\onedot} \def\Ie{{I.e}\onedot}
\def\cf{{c.f}\onedot} \def\Cf{{C.f}\onedot}
\def\etc{{etc}\onedot}
\def\vs{{vs}\onedot}
\def\wrt{w.r.t\onedot}
\def\dof{d.o.f\onedot}
\def\etal{{et al}\onedot}
\makeatother
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
\begin{frame}{}
\begin{columns}[t]
\begin{column}{.3\linewidth}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{block}{Introduction}
\begin{itemize}
\item automatic sign language recognition system %what
\item \alert{necessary for communication} between deaf and
hearing people
\item \alert{continuous} sign language recognition,
\alert{several} speakers, \alert{vision-based} approach, \alert{no
special hardware}
\item large vocabulary speech recognition (LVSR) system to
obtain a textual representation of the signed
sentences
\item evaluation of speech recognition techniques on \alert{publicly
available sign language
corpus}
\end{itemize}
\end{block}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{block}{Automatic Sign Language Recognition (ASLR)}
\begin{columns}[T]
\begin{column}{.49\linewidth}
\begin{itemize}
\item \alert{similar to speech recognition}: temporal sequences of images
\item important features
\begin{itemize}
\item hand-shapes, facial expressions, lip-patterns
\item orientation and movement of the hands, arms or body
\end{itemize}
\item HMMs are used to compensate time and amplitude variations of the signers\par
\vskip2ex
\centerline{\includegraphics[width=.5\linewidth]{dreuw/hmm}}
\end{itemize}
\end{column}
\begin{column}{.49\linewidth}
\begin{itemize}
\item \alert{goal:} find the model which best expresses the observation sequence
\end{itemize}
\vskip2ex
\includegraphics[width=\linewidth]{dreuw/xfigures/BayesArchitectureSignLanguage_Dreuw_01Jun06}
\end{column}
\end{columns}
\end{block}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{block}{Experimental Setup}
\begin{columns}[t]
\begin{column}{.75\linewidth}
\noindent{\hskip1cm\textbf{Database}}
\begin{itemize}
\item system evaluation on the RWTH-BOSTON-104 database
\begin{itemize}
\item \alert{201 sentences} (161 training and 40 test sequences)
\item vocabulary size of \alert{104 words}
\item 3 speakers (2 female, 1 male)
\item corpus is annotated in glosses
\end{itemize}
\end{itemize}
\vskip1ex
\noindent{\hskip1cm\textbf{Problems}}
\begin{itemize}
\item 26\% of the training data are \alert{singletons}
\item simple sentence structure
\item one out-of-vocabulary (OOV) words with whole-word models
\end{itemize}
\vskip1ex
\noindent{\hskip1cm\textbf{Differences in Comparison to ASR}}
\begin{itemize}
\item simultaneousness % multi-channel ... but unclear if necessary
\item signing space % verb flexion, negation, ...
\item environment % cluttered background, clothes, lighting, ... different microphones in ASR?
\item speakers and dialects % as in ASR
\item coarticulation and movement epenthesis %
\item silence % unclear as there might be no energy changes in signal but still information, e.g. holded signs
\item whole-word models and sub-word units % necessary for large-vocabulary systems
\end{itemize}
\end{column}
\begin{column}{.25\linewidth}
\vskip0ex
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\centering
\includegraphics[width=0.7\linewidth]{images/figures/Boston104-1_Dreuw_01Jun06}\\[1ex]
\includegraphics[width=0.7\linewidth]{images/figures/Boston104-2_Dreuw_01Jun06}\\[1ex]
\includegraphics[width=0.7\linewidth]{images/figures/Boston104-3_Dreuw_01Jun06}\\[1ex]
\includegraphics[width=0.7\linewidth]{images/u-signlanguage-BOSTON104-png-png-segments-190_fn000060-0}\\[1ex]
\end{column}
\end{columns}
\end{block}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\end{column}
\begin{column}{.3\linewidth}
\begin{block}{System Overview}
\vfill
\noindent{\textbf{Visual Modeling (VM)}}
\begin{itemize}
\item related to the acoustic model in ASR
\item HMM based, with separate GMMs, globally pooled diag. covariance matrix
\item monophone whole-word models
\item pronunciation handling
\end{itemize}
\vskip1ex
\noindent{\textbf{Language Modeling (LM)}}
\begin{itemize}
\item according to ASR: LM should have a greater weight than the VM
\item trigram LM using the SRILM toolkit, with modified Kneser-Ney discounting with interpolation
\end{itemize}
\vskip1ex
\begin{columns}[t]
\begin{column}{.5\linewidth}
\noindent{\hskip1cm\textbf{Features}}\par
\begin{itemize}
\item \alert{appearance-based image features}: for baseline system
\begin{itemize}
\item thumb\-nails of video se\-quen\-ce fra\-mes (intensity images scaled to 32x32 pixels)
\item give a global description of all (manual and non-manual) features proposed in linguistic research
\end{itemize}
\item \alert{manual features}:
\begin{itemize}
\item dominant hand \alert{tracking}: hand position,
hand velocity, and hand trajectory features
\end{itemize}
\end{itemize}
\end{column}
\begin{column}{.5\linewidth}
\vskip0ex
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\centering
\includegraphics[height=0.33\linewidth]{images/u-signlanguage-BOSTON104-videoBank-camera0-001_0_fn000054-0}
\quad\quad
\includegraphics[height=0.33\linewidth]{images/u-signlanguage-BOSTON104-videoBank-camera0-090_0_fn000056-0}
\vskip3ex
\includegraphics[height=0.4\linewidth]{images/graphs/trajectory_45_54}
\quad
\includegraphics[height=0.4\linewidth]{images/graphs/trajectory_25_34}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\end{column}
\end{columns}
\end{block}
\begin{block}{Feature Selection and Model Combination}
\begin{columns}[T]
\begin{column}{.35\linewidth}
\noindent{\hskip1cm\textbf{Feature Selection}}\par
\begin{itemize}
\item \alert{concatenation} of appearance-based and manual features
\item \alert{sliding window} for context modeling
\item \alert{dimensionality reduction} by PCA and/or LDA
\end{itemize}
\end{column}
\begin{column}{.65\linewidth}
\raggedleft
\includegraphics[width=.95\linewidth]{images/xfigures/CompositeFeature_Dreuw_28Sep06}%
\end{column}
\end{columns}
\vskip5ex
\begin{columns}[T]
\begin{column}{.35\linewidth}
\noindent{\hskip1cm\textbf{Model Combination}}\par
\begin{itemize}
\item \alert{log-linear combination} of independently
trained models
\item profit from independent alignments (\eg performing well for long and short words)
\item profit from different feature extraction approaches
\end{itemize}
\end{column}
\begin{column}{.65\linewidth}
\raggedleft
\includegraphics[width=\linewidth]{images/xfigures/CompositeModels_Dreuw_17Apr07}
\end{column}
\end{columns}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\end{block}
\end{column}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{column}{.3\linewidth}
\begin{block}{Experimental Results}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\centering
\includegraphics[width=0.33\linewidth]{images/pca-ldaw/ldaw}%
\includegraphics[width=0.33\linewidth]{images/pca-pcaw/pcaw}%
\includegraphics[width=0.33\linewidth]{images/language-model/lm-scale}%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}
\centering
%\footnotesize
%\caption{Baseline results using appearance-based features}
\begin{tabular}{@{} p{.7\linewidth} r r @{}}
\toprule
Features & Dim. & [\%WER] \\
\midrule
frame intensity (w/o pronunciations) & 1024 & 54.0 \\%\cite{zahedi06bmvc}
frame intensity (w/ pronunciations) & 1024 & 37.0 \\
frame intensity (w/ pronunciations + tangent distance) & 1024 & 33.7 \\% \cite{dreuw06smvp}
PCA-frame & 110 & 27.5 \\
%PCA-frame, hand-acceleration & 112 & 26.9 \\
PCA-frame, hand-position & 112 & 25.3 \\
PCA-frame, hand-velocity & 112 & 24.2 \\
PCA-frame, hand-trajectory & 112 & 23.6 \\
\addlinespace
\addlinespace
model-combination & 2x100 & 17.9 \\
\bottomrule
\end{tabular}
\label{tab:baseline-results}
\end{table}
\end{block}
\vskip-2ex
\begin{columns}[t]
~~
\begin{column}{.57\linewidth}
\begin{block}{Example Results}
\noindent{\hskip1cm\textbf{Correct Examples}}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
IX-1P & \ \ FIND & \ \ SOMETHING-ONE & \ \ BOOK\\
\textcolor{black}{IX-1P} & \ \ \textcolor{black}{FIND} & \ \ \textcolor{black}{SOMETHING-ONE} & \ \ \textcolor{black}{BOOK}
\end{tabular}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
JOHN & \ \ FISH & \ \ WONT & \ \ EAT & \ \ BUT & \ \ CAN & \ \ EAT & \ \ CHICKEN\\
\textcolor{black}{JOHN} & \ \ \textcolor{black}{FISH} & \ \ \textcolor{black}{WONT} & \ \ \textcolor{black}{EAT} & \ \ \textcolor{black}{BUT} & \ \ \textcolor{black}{CAN} & \ \ \textcolor{black}{EAT} & \ \ \textcolor{black}{CHICKEN}
\end{tabular}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c}
LOVE & \ \ JOHN & \ \ WHO\\
\textcolor{black}{LOVE} & \ \ \textcolor{black}{JOHN} & \ \ \textcolor{black}{WHO}
\end{tabular}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
JOHN & \ \ BUY & \ \ YESTERDAY & \ \ WHAT & \ \ BOOK\\
\textcolor{black}{JOHN} & \ \ \textcolor{black}{BUY} & \ \ \textcolor{black}{YESTERDAY} & \ \ \textcolor{black}{WHAT} & \ \ \textcolor{black}{BOOK}
\end{tabular}
\hrule
\vskip2ex
\noindent{\hskip1cm\textbf{Incorrect Examples}}\par
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
MARY & \ \ VEGETABLE & \ \ KNOW & \ \ IX & \ \ LIKE & \ \ CORN\\
\textcolor{black}{MARY} & \ \ \textcolor{black}{VEGETABLE} & \ \ \textcolor{black}{KNOW} & \ \ \textcolor{black}{IX} & \ \ \textcolor{black}{LIKE} & \ \ \textcolor{red}{MARY}
\end{tabular}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
JOHN & \ \ IX & \ \ GIVE & \ \ MAN & \ \ IX & \ \ NEW & \ \ COAT\\
\textcolor{black}{JOHN} & \ \ \textcolor{black}{IX} & \ \ \textcolor{red}{WOMAN} & \ \ \textcolor{black}{\underline{\phantom{MAN}}} & \ \ \textcolor{black}{\underline{\phantom{IX}}} & \ \ \textcolor{black}{NEW} & \ \ \textcolor{black}{COAT}
\end{tabular}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
& \ \ LIKE & \ \ CHOCOLATE & \ \ WHO\\
\textcolor{green}{JOHN} & \ \ \textcolor{black}{LIKE} & \ \ \textcolor{black}{CHOCOLATE} & \ \ \textcolor{black}{WHO}
\end{tabular}
\hrule
\begin{tabular}{@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c@{}>{\small}c}
JOHN & \ \ [UNKNOWN] & \ \ & \ \ BUY & \ \ HOUSE\\
\textcolor{black}{JOHN} & \ \ \textcolor{red}{FUTURE} & \ \ \textcolor{green}{NOT} & \ \ \textcolor{black}{BUY} & \ \ \textcolor{black}{HOUSE}
\end{tabular}
\hrule
\vspace{-1ex}
\end{block}
\end{column}
~
\begin{column}{.4\linewidth}
\begin{block}{RWTH-BOSTON-104 Database}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\noindent{\hskip1cm\textbf{Corpus Statistics}}
\begin{table}
\centering
%\footnotesize
%\caption{Corpus Statistics}
\begin{tabular}{@{} p{.5\linewidth} r r @{}}
\toprule
& Training & Test \\
\midrule
sentences & 161 & 40 \\
running words & 710 & 178 \\
frames & 12422 & 3324 \\
vocabulary & 103 & 65 \\
singletons & 27 & 9 \\
OOV & - & 1 \\
\bottomrule
\end{tabular}
\end{table}
\vskip2ex
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\noindent{\hskip1cm\textbf{LM Perplexities}}
\begin{table}
\centering
%\caption{}
\begin{tabular}{@{} p{.8\linewidth} r @{}}
\toprule
LM type & $PP$ \\
\hline
zerogram & 106.0 \\
unigram & 36.8 \\
bigram & 6.7 \\
trigram & 4.7 \\
\bottomrule
\end{tabular}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\vskip2ex
Database is publicly available
\end{block}
\end{column}
\end{columns}
\begin{block}{Conclusion}
\begin{itemize}
\item LVSR system is suitable for vision-based continuous sign language recognition
\item many of the principles known from ASR can directly be transfered
\item important for ASLR: temporal contexts, pronunciation handling, language modelling, and model combination
\item \alert{outlook:} connection of recognizer output to a
statistical machine translation system achieved promising
translation results
\end{itemize}
\vspace{-1ex}
\end{block}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\end{column}
\end{columns}
\end{frame}
\end{document}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Local Variables:
%%% mode: latex
%%% TeX-PDF-mode: t
%%% End:
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