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front end analysis of speech recognition_a review.pdf (2930 KB) 
Thursday, January 20, 2011
[Misc] Command Line Keyboard Shortcuts for Mac OS X
From: http://osxdaily.com/2006/12/19/command-line-keyboard-shortcuts-for-mac-os-x/
The command line in Mac OS X can be a very powerful and fun tool, so it’s good to know how to maneuver around if you find yourself in it. By default, the Mac OS X Terminal uses the Bash shell, which is what these keyboard shortcuts are intended for. So if you’re ready to get your feet wet, open up the Terminal and try these shortcuts out, they’re sure to make your command line life easier. The list isn’t too crazy so you should be able to try all these out within a minute or two, have fun:
| Ctrl + A | Go to the beginning of the line you are currently typing on |
| Ctrl + E | Go to the end of the line you are currently typing on |
| Ctrl + L | Clears the Screen, similar to the clear command |
| Ctrl + U | Clears the line before the cursor position. If you are at the end of the line, clears the entire line. |
| Ctrl + H | Same as backspace |
| Ctrl + R | Let’s you search through previously used commands |
| Ctrl + C | Kill whatever you are running |
| Ctrl + D | Exit the current shell |
| Ctrl + Z | Puts whatever you are running into a suspended background process. fg restores it. |
| Ctrl + W | Delete the word before the cursor |
| Ctrl + K | Clear the line after the cursor |
| Ctrl + T | Swap the last two characters before the cursor |
| Esc + T | Swap the last two words before the cursor |
Wednesday, January 19, 2011
[Tool] HMM Toolkit STK from Speech@FIT
http://www.fit.vutbr.cz/research/groups/speech/sw/stk.html
Simple example scripts for MMI, MPE training.
[Speech] Hierarchical structures of neural networks for phoneme recognition
Four Neural Network based phoneme recognition systems are investigated:
a) the TRAPs system (Fig. 1a) - separate networks for processing of speech in frequency bands;
b) the split temporal context (STC) system (Fig. 1b) - separate networks for processing of blocks of spectral vectors;
c) combination of both (Fig. 1c) - split in both frequency and time.
d) Tandem of two networks, the frond-end network is trained in classical ways and the back-end is trained on the combination of the front-end's posteriors and original features.
The assumptions for those systems are:
a) Independent processing of speech in critical bands;
b) Independent processing of parts of phonemes;
c) both a) and b).
Phoneme strings are basic representation for automatic language recognition and it is proved that language recognition results are highly correlated with phoneme recognition results. Phoneme posteriors are useful representation for acoustic keyword search, they contain enough information to distinguish among all words and they are small enough to store compared for example to the size of posteriors from context dependent Gaussian Mixture Models.
Two ways to provide additional information for NN training:
i) windowing, multiple frames context window, hamming window to emphasis the central frame;
ii) output representation: some improvements have been observed when a net was trained for multiple tasks in the same time.
A special Phoneme set mapping adopted in this paper is they merged closures with burst instead of with silence (bcl b -> b not bcl b -> pau b). It is believed that this mapping is more appropriate for features which use a longer temporal context.
The number of neurons in hidden layer of neural networks was increased until the saturation of phoneme error rate (PER) was observed. The obtained number of hidden layer neurons was approximately 500.
Table 1 shows the superiority of long Mel-bank energies but also great improvement coming from three state model. ( Block of 31 vectors of mel-bank energies (MBE) = 310 ms, Temporal trajectories in bands were weighted by Hamming window and down-sampled by DCT to 11 coefficients. )
The final best PER reported in this paper is using the 5-block STC system with bigram LM as shown in following table:
Monday, January 17, 2011
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