[Points: 8; Issued: 2004/06/17; Deadline: 2004/06/28; Tutor:
Bernhard Tittelbach, Thomas Zilaji; Infohour:
2004/06/21, 12:00-13:00, Seminarraum IGI; Einsichtnahme:
2004/07/05, 12:00-13:00, Seminarraum IGI; Download: pdf;
In this homework you shall build a simple automatic speech
recognition (ASR) system for the German words ``ja'' and ``nein''.
The system is realized using the hidden Markov model toolkit HTK.
Recordings of the words ``ja'' and ``nein'' from many different
speakers are available, for training these recodings have been
pre-processed for you (to reduce the zip file size and computation
time) to yield speech feature vectors
in the form of mel frequency cepstral coefficients (MFCC, files
*.mfc) which are commonly used in ASR systems. Only for testing a
limited number of ``ja''/``nein'' examples are included in
ASR.zip as waveform signals (files Q/*.wav).
Our ASR system will model the two words by a sequence of
monophone models (one HMM with three states for each phone). The
emission probabilities for the HMM states are Gaussian pdfs.
To carry out this homework you need some programs from HTK, and
some script files (perl-scripts for linux, batch-files for
windows). Download the appropriate zip files from the homework
assignment page. Note, that the files have not been extensively
tested yet! If you encounter problems running the scripts, contact
us. If you want to use HTK beyond this homework, it is (freely)
available at http://htk.eng.cam.ac.uk/.
Note that HTK commands and script files are called from the
command line (DOS window).
As always: Write down the results and your observations for all
experiments you performed, including the chosen settings, and your
- Do you get an improvement using the larger training set as
compared to the result using the small training set? How many
training iterations seem reasonable, in the case of the small/large
- If you wanted to build a YES/NO recognizer, would you use
monophone-models, as we did, or do you think whole-word models are
more suitable? Give reasons!