1. Jacob Zurasky ECE5526 – Spring 2011
Acoustic Modeling
Jacob Zurasky
ECE5526 – Spring 2011

2. Project Goals Generate an Acoustic Model for digit recognition
Create a MATLAB tool to transcribe training data to be used in model generation
Create a MATLAB tool to generate an Acoustic Model based on the training data and transcriptions

3. What is an Acoustic Model?
A statistical representation of sounds that make up a word.
Created from feature vectors that are extracted from the training data

4. Feature Vectors
For each window of speech (~10ms), compress speech data to 39 MFCC’s
Impossible to directly compare speech samples to identify characteristics

5. MFCC’s Mel Frequency Cepstral Coefficients
Models the sound samples in a way that is similar to the way the human ear perceives sound
Utterance is broken into windows, ~10mS
Apply a hamming window to the section

6. MFCC’s FFT to obtain the log frequency spectrum
Mel Filters applied to log FFT output
DCT applied to Mel Filter output
Result is 12 MFCC’s
Compute energy of the signal

7. Training Data – ‘mark_words.m’
Obtain data containing the words you wish to build an acoustic model for
Create a transcription file for each utterance

8. Training Data – ‘model.m’
For each part of a word to be analyzed, collect all feature vectors associated
Repeat for each utterance
Calculate the means and variances for each dimension of the feature vector for a given part of the word

9. Training Data – ‘evaluate.m’
Implements classification of sound
Compares current feature vector to the acoustic model
Determines if probability is high enough to transition states

10. Issues Encountered Small training data set
Some digits need more than two sections
Time constraints of making all transcriptions
1,3,5,7 models fairly reliable
2,4,6 models need refinement

11. Future Goals Implement true HMM system for classification
Add automated training and re-evaluation of the HMM
Apply techniques to analysis of breathing sounds for sleep disorder recognition