\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}
