PROBLEM 4:

FEATURE ANALYSIS

The goal of this assignment is to teach you the fundamentals of the signal processing component of a speech recognition system. We will focus on two techniques: mel frequency-spaced cepstral coefficients (MFCCs) and filter bank amplitudes (FBAs).

• Generate a synthetic vowel by summing three sinewaves of equal amplitudes at frequencies of 500, 1500, and 2500 Hz. Process these through a standard MFCC front-end (details will follow in class), and through a front-end using log spectral amplitudes directly (followed by a global principal components analysis of course). Show that the results make sense based on theoretically predicted values of the data.
  
  
• Generate a bandpass filter with the above center frequencies and 200 Hz bandwidths. Process Guassian white noise through this filter. Perform feature analysis on this data using the two front-ends above, and show that the results make sense.
  
  
• Compute MFCC features on a typical set of SWB files, and determine if the diagonal covariance matrix approximation used in variance-weighting makes sense.

Details on the locations of data, etc., will follow shortly.