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\begin{document}% -------------------------------------------------------------
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% cover page
\title{{\sc \large Speaker \& Speech Recognition (EEEM034) assignment}\\ [2ex]
{\bf HMM decoding and training report}}
\author{Student: Joe Bloggs}
\date{URN: 123456789}
\maketitle

\vspace{2ex}
\begin{abstract}
This is a LaTeX template that you can use to write the assignment report.
\end{abstract}

\vfill

\centerline{\sc Faculty of Engineering \& Physical Sciences}\vspace{2ex}
\centerline{\sc Department of Electronic Engineering}\vspace{2ex}
\centerline{\sc Lecturer: Dr Philip Jackson}\vspace{4ex}

\noindent
I confirm that the coursework I am submitting is entirely my own work and that all material attributed to others has been clearly identified, fully acknowledged and referenced to its original sources.\\ [8ex]
{Signature: \rule{30ex}{0.1ex}}\hfill{Date: \rule{15ex}{0.1ex}}\\

% table of contents
\newpage
\tableofcontents


% body of report
\newpage
\section{Introduction}

A brief description of the assignment is given here.

\begin{figure}[hbt]
  \centerline{\includegraphics[width=110mm]{../fig/hmm14a.pdf}}
  \caption{\small HMM state topology for the prototype, including entry and exit null states.}
\end{figure}

\newpage
\section{Method}

This section provides a statement of the equations used to calculate the results and any important implementation details.
A lot of the method is described in Jackson's lecture notes \cite{Jackson2010}.
An example is given in the following equation:
\begin{equation}\label{eq1}
P ( \Ob | \lambda) = \sum_{\mbox{\scriptsize all $X$}} P ( \Ob, X | \lambda) \,\approx\, P ( \Ob, X^* | \lambda).
\end{equation}
We can now refer to this as Eq.~\ref{eq1}.


\newpage
\section{Results}

This section gives a complete set of the results for each point, typically in tables.

\subsection{Running the Viterbi algorithm}

An example of a table is given in Table~\ref{tab1}.

\begin{table}[hbt]
  \centerline{
    \begin{tabular}{l|ll}
    Operation  & MFCC & PLP-CC  \\ [0ex] \hline
    {\em Pre-emphasis} & 6\,dB/octave of waveform & equal-loudness of spectrum \\ [0ex]
    {\em Filter bank} & triangular mel filters & critical-band filters  \\ [0ex]
    {\em Amplitude compression} & logarithmic & cube-root  \\ [0ex]
    {\em Spectral smoothing} & Cepstral & LPC smoothing\\ \hline
    \end{tabular}
  }
  \caption{\label{tab1}\small Comparison of MFCC and PLP-CC features.}
\end{table}

\subsection{Calculating likelihoods for Baum-Welch training}

\subsection{Re-estimating the model and evaluating the new best path}


\newpage
\section{Conclusion}

This includes a summary of what you did, statement of the main findings and a short discussion of any points of interest.


\newpage
\bibliographystyle{plain}
\bibliography{assignment_refs} 

\newpage
\section{Appendix}

You can include any intermediate results or evidence of your working here, along with program listings for any code that you have authored.

\end{document}	% ---------------------------------------------------------
