--- /dev/null
+DOCUMENTS:=exam
+PDFLATEXFLAGS:=-halt-on-error
+PDFLATEX:=pdflatex $(PDFLATEXFLAGS)
+
+all: $(addsuffix .pdf,$(DOCUMENTS))
+
+%.pdf: %.tex q1.tex q2.tex q3.tex
+ $(PDFLATEX) $<
+ $(PDFLATEX) $<
+
+clean:
+ $(RM) -v $(addprefix $(DOCUMENTS),.pdf .log .aux)
--- /dev/null
+\documentclass[titlepage,a4paper]{article}
+
+\usepackage{rutitlepage}
+\usepackage{geometry}
+\usepackage{enumitem}
+\usepackage{listings}
+
+\title{Midterm Exam}
+\author{Mart Lubbers\\s4109503}
+\date{\today}
+
+\begin{document}
+\maketitleru[%
+ course={Introduction to Language and Speech Technology},
+ authorstext={Author:}]
+\begin{enumerate}
+ % Question 1
+ \item\input{q1.tex}
+
+ \newpage
+ % Question 2
+ \item\input{q2.tex}
+
+ \newpage
+ % Question 3
+ \item\input{q3.tex}
+\end{enumerate}
+
+\end{document}
--- /dev/null
+\begin{enumerate}
+ % Question 1a
+ \item Disfluencies are annotated by surrounding them with square braces.
+ The first bit shows the \emph{reparandum}, the second bit denoted with
+ the \texttt{+} shows the \emph{editing phase} and the last bit shows
+ the \emph{repair}. We want to only keep the repair since that depicts
+ the correct, meant by the speaker, speech.
+
+ \verb#s/\[.*?\+\{.*?\}(.*?)\]/\1/g#
+
+ Bit by bit:
+ \begin{itemize}
+ \item \verb#s/# Substitution.
+ \item \verb#\[# Matches the opening square bracket. We escape this
+ because \verb#[# is a regular expression control character and
+ we want to match a literal.
+ \item \verb#.*?\+# Matches non-greedily everything up to the plus
+ mark. Thus the \emph{reparandum}. Note that the
+ \emph{reparandum} can be empty (in case the speaker immediately
+ start editing). We escape the \verb#+# for the same reason as
+ the previous segment.
+ \item \verb#\{.*?\}# Matches everything between the curly braces.
+ Thus the \emph{editing phase}. Note again that this match can
+ only contain empty curly braces since the \emph{editing phase}
+ can be empty.
+ \item \verb#(.*?)# Matches non-greedily everything up to the
+ closing square brace and captures it in the group. Thus the
+ \emph{repair}. Note that we do not require this group to be the
+ exact same as the \emph{reparandum}.
+ \item \verb#\]/# Matches the closing square bracket and we proceed
+ to the replacement. We escape this for the same reason as
+ before.
+ \item \verb#\1/g# We replace the entire match with only the
+ captured \emph{repair} group and do this globally since there
+ can be multiple repairs in an utterance.
+ \end{itemize}
+
+ % Question 1b
+ \item \textsc{MEMM}'s use features to add extra information to words.
+ \textsc{IOB} tagging is a partial parsing or chunking method that only
+ discriminates between \emph{Beginning} (\texttt{B}), \emph{Internal}
+ (\texttt{I}) and \emph{Outside} (\texttt{O}) categories.
+
+ Say we use the same segmentation as before, we should mark the
+ \emph{reparandum} and \emph{editing phase} as \emph{Outside}
+ (\texttt{O}) parts and the repair should be parsed as usual. Note that
+ a chunk then can include \texttt{O} marked segments. For example in ``a
+ car uh plane'' the ``car uh'' part will be tagged as \texttt{O}, ``a''
+ as \texttt{B\_NP} and ``plane'' as \texttt{I\_NP}.
+
+ For the algorithms it might be necessary to add a different tag to
+ denote internal \texttt{O} segments. This can be done by adding a
+ suffix to the \texttt{O} tag. In the previous example the text will
+ then be chunked as: \texttt{B\_NP O\_NP I\_NP}.
+
+ Concerning the \textsc{MEMM} features, obviously editing phase segments
+ should be marked as such but also the reparandum should be tagged as
+ such to not confuse it with a regular segment.
+
+ % Question 1c
+ \item Repairs are only noticed when you can lookahead to the \emph{editing
+ phase} markers. It might be necessary to either lookahead a little bit
+ or to work outwards from the identified \emph{editing phase}.
+ Right-to-left has the same problem as left-to-right in the sense that
+ it will see the repair first and also has to lookahead to know whether
+ it is part of a repair.
+\end{enumerate}
--- /dev/null
+\begin{enumerate}
+ % Question 2a
+ \item This can be achieved by adding disfluency rules to the \textsc{CFG}.
+ This has to be done for all rules that can possible produce
+ disfluencies. Most likely only the lowest level of rules (unit
+ productions) need such disfluency structures. For example, if we would
+ do it for the rule that transforms a \texttt{Noun} into a word it would
+ look like this:
+
+ \begin{lstlisting}
+Noun -> TrueNoun | EditNoun TrueNoun
+TrueNoun -> flight | ...
+
+EditNoun -> TrueNoun EditWord
+EditWord -> uh | ...
+ \end{lstlisting}
+
+ With feature structures this can be generalized and have less
+ ambiguitiy. Features can for example force the \emph{Reparandum} to be
+ of the same \texttt{CAT} as the \emph{Repair} and disfluencies might
+ have some constraints that can also be expressed with features.
+
+ % Question 2b
+ \item Standard \textsc{CKY} parsing only works for grammars in
+ \emph{Chomsky Normal Form} (\textsc{CNF}). This means that the tree
+ returned will not exactly represent the \textsc{CFG} since it possibly
+ had to be converted to \textsc{CNF}. To adapt \textsc{CKY} in a
+ fundamental way so that it correctly parses repair structures would be
+ very difficult, albeit impossible. It basically means that, in the
+ deepest loop, you have to build in functionality that is similar to the
+ grammar that recognizes such structures and behave accordingly. While
+ this is probably theoretically possible, it will result in a different
+ algorithm that has a hard-coded sub-grammar in itself.
+
+ % Question 2c
+ \item Similar to the previous sub-question; while it is possible to make the
+ \emph{Predictor} more smart and add disfluency structures to the chart
+ it would change the \emph{Earley} algorithm significantly. The change
+ of the algorithm would also be very specific to certain disfluency
+ structures and makes it possibly unusable for languages that do not
+ have such structures. Note that it is more easy to add this to an
+ \emph{Earley} parser compared to adding it to an \emph{CKY} parser. For
+ an \emph{Earley} parser it just means hard-coding some extra grammar
+ rules in the \emph{Predictor}. For \emph{CKY} it means transforming
+ the rules to specific transformations in the table which might not be
+ trivial.
+\end{enumerate}
--- /dev/null
+\begin{enumerate}
+ % Question 3a
+ \item In an \emph{ASR} system we can expect problems in several phases.
+
+ The first phase of an \emph{ASR} is just extracting the features. We
+ do not expect problems there since it will just produce slightly
+ different features for some part but that is not something the feature
+ extraction cares about. It just objectively has to extract features and
+ since it is still human speech, there are no problem with disfluencies.
+
+ When trying to transform the cepstral features into a sentence several
+ components are involved. First a phone likelihood is calculated, we
+ maybe expect slight problems here since even the phones might be
+ reduced and the \emph{editing phase} words could just be rudimentary
+ sounds instead of phones and thus it might select suboptimal
+ likelihoods.
+
+ When decoding the phone likelihood into words a lexicon is used. This
+ lexicon might not contain the edit words and possibly also not the
+ reduced \emph{reparanda}.
+
+ Finally during \emph{Viterbi} \emph{N-Gram} models come into play and
+ if they are not extracted from a dataset that also included
+ disfluencies it might be the case that the probabilities of
+ disfluencies appearing are so low that it tries to fit similarly
+ sounding real words instead of the disfluency.
+
+ % Question 3b
+ \item Solving the problem of phone likelihood computation can be done by
+ shrinking the window of the feature extraction so that strongly reduced
+ phones are also correctly recognized.
+
+ Solving the second problem can be done by adding disfluency words to
+ the lexicon and also more reductive pronunciations of words.
+
+ Lastly we can increase the decoding performance by specifically
+ extracting the \emph{N-Gram} probabilities from data that also contains
+ disfluencies.
+
+ % Question 3c
+ \item To add disfluencies to speech synthesis one must know how they arise.
+ There are some word categories that have more disfluencies than others.
+ Also they may be produced to give speaker some more time to think about
+ the rest of the sentence. When you know such properties of disfluencies
+ you can model them in the speech synthesis in the normalization phase.
+
+ In the normalization phase the system can add disfluencies at sections
+ that often produce them. This most likely is the most effective in
+ tokenisation. Specific tokens that can be selected to be expanded to a
+ disfluency.
+
+ Later on in the pipeline the system must also be adapted. Namely in the
+ waveform synthesis. Depending on the technique applied some
+ improvements can be done. When the synthesis technique is unit
+ selection it might be helpful to have units for common disfluencies and
+ at least units for \emph{editing phase} words. It might also be helpful
+ to add units that represent a reduced pronunciation to be used in the
+ \emph{reparandum}. When \emph{diphone synthesis} is used there do not
+ have to be big changes to be applied since most likely the diphone
+ combinations already exist in the database.
+\end{enumerate}
--- /dev/null
+% Radboud University Nijmegen titlepage
+% Author: Mart Lubbers
+% Date: 2016-06-28
+%
+% TODO
+% - Internationalize (dutch logos)
+% - Nice document
+% - Make CTAN ready
+\RequirePackage{graphicx,ifpdf,keyval}
+
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+ institute={Radboud University Nijmegen},
+ authorstext={Authors:},
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+}
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+}
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