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Jacob Zurasky ECE5526 – Spring 2011
Acoustic Modeling Jacob Zurasky ECE5526 – Spring 2011
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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
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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
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Feature Vectors For each window of speech (~10ms), compress speech data to 39 MFCC’s Impossible to directly compare speech samples to identify characteristics
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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
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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
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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
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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
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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
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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
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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
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