\begin{table*}
\end{table*}
and that will make the figure span the width of the entire page.
The amount of evidence required to accept that an event is unlikely to have arisen by chance is known as the significance level or critical p-vale. In traditional Fisherian statistical hypothesis testing, the p-value is the probability conditional on the null hypothesis of the observed data or more extreme data.
If the obtained p-value is small then it can be said either the null hypothesis is false or an unusual event has occurred.
In SCTK toolkits, the Null Hypothesis is:
There is no performance difference between the two systems.
Thus the p-value is, assuming the two system have no difference, the probability of the test statistic having a value at least as extreme as that actually found, is no more than p-value.
So, the small the p-value is, the more statistically significant the system is.
In the attached paper, the authors combine two speaker adaptation methods to improve the performance of speech synthesis of cross-lingual experiments.
The system are HMM based, and the two methods are:
1) Decision Tree Marginalization
2) HMM State Mapping
From: http://publications.idiap.ch/downloads/papers/2009/Liang_ICASSP_2010.pdf
The overall system they used:
Different processing combinations:
The best is deltas+PCA+normalize.
The architecture is:
The training procedure:
Discussion:
Details are in the attached paper.
Add following Eclipse Galileo Repository:
http://download.eclipse.org/releases/galileo
to 'Available Software Sites'.
Then select from the Programming Language, the JDT.
Following are some repositories:
Eclipse Galileo Repository - http://download.eclipse.org/releases/galileo
galileo - http://download.eclipse.org/tools/cdt/releases/galileo
update site - http://www.eclipse.org/jdt/core/update-site
List of different methods mentioned in their paper:
In Sung et.al.'s paper, they used Hidden Conditional Random Field for phone recognition on TIMIT, and achieved 28.3% PER.
In trn format, each line is an utterance with the utterance id in parentheses after it.
e.g:
I LIKE ICE CREAM (c02abd30)
Step I: Generate Alignment using sclite
e.g:
sclite -F -i wsj -r ref.trn -h w15_bg.trn -o sgml
-F scores segments as correct instead of "-d" which uses "diff" for differences;
-i sets the utterance id type to be wsj (Wasll Street Journey)
-r sets the reference transcription to be file "ref.trn"
-h sets the recognized results file to be "w15_bg.trn"
-o sets the output result format to be "sgml"
The output is a file named "w15_bg.trn.sgml".
Note: for each experimental result file, we have to align it to the reference to generate a "sgml" format file.
Step III: Significance Test using sc_stats
e.g:
cat w15_bg.trn.sgml w15_tg.trn.sgml | sc_stats -p -t mapsswe -v -u -n result_bg_tg
-p reads from stdin, the piped output;
-t specifies the test to be mapsswe (the Matched Pairs Sentence Segment Word Error Test)
-v performs the tests on a pair of hypothesis files
-u unifies the test instead of creating comparison matrix for each test
-n output report file name
Appendix:
sc_stats options:
The commandline options for sc_stats can be broken into four categories:
- The 'range' graphs are an ASCII representation of the of the variablity in error rates for a given speaker. The graph is sorted be the mean of statistic computed for each speaker. EXAMPLE
- The 'grange' graph is a gnuplot version of the same data ploted in 'range. There are two sets of files created. The first set, which is called '*.grange.spk.plt' and '*.grange.spk.dat', contains the gnuplot command files and data files respectively for the speaker performance variability across systems graph. The second set, which is called '*.grange.sys.plt' and '*.grange.sys.dat', contains the gnuplot command files and data files respectively for the system performance variability across speakers graph. EXAMPLE
- The 'grange2' graph is similar to the 'grange' graph except that each systems speaker word error scores are identified by a unique symbol. EXAMPLE
The chmod command uses a three-digit code as an argument.
The three digits of the chmod code set permissions for these groups in this order:
Each digit of this code sets permissions for one of these groups as follows. Read is 4. Write is 2. Execute is 1.
The sums of these numbers give combinations of these permissions:
| Command | Purpose |
|---|---|
| chmod 700 apple.txt | Only you can read, write to, or execute apple.txt |
| chmod 777 apple.txt | Everybody can read, write to, or execute apple.txt |
| chmod 744 apple.txt | Only you can read, write to, or execute apple.txt Everybody can read apple.txt; |
| chmod 444 apple.txt | You can only read apple.txt, as everyone else. |
You can use the ls command with the -l option to show the file permissions set. For example, for apple.txt, I can do this:
$ ls -l apple.txt
-rwxr--r-- 1 december december 81 Feb 12 12:45 apple.txt
$
The sequence -rwxr--r-- tells the permissions set for the file apple.txt. The first - tells that apple.txt is a file. The next three letters, rwx, show that the owner has read, write, and execute permissions. Then the next three symbols, r--, show that the group permissions are read only. The final three symbols, r--, show that the world permissions are read only.
