This study was on the http://mphmedia.net web site, which is no longer active.  I am posting it because it contains some excellent comparison information for HTK mfcc targetkinds (before it gets flushed from Google's cache):

The goal is to compare all the possible mfcc TARGETKINDs. It may be that some of those are missing. This is an initial study for which feedback has not been used to correct and improve the results. The low recognition results of the models should not reflect on their potential in that the study was designed for TARGETKIND comparative purposes using a minimum of acoustic data to reduce the model assembly and testing time.

The wav files and their transcriptions were obtained from VoxForge: 8khz 16bit wav files, 1402 speakers, 4120 sentences, 42140 words.

The acoustic model creation method is one that was provided by VoxForge with their acoustic model toward the beginning of 2009. This linkprovides a version I have assembled from the VoxForge method. The link does not show the actual method used for this report but is the basis for it. The primary HTK sequences are the same.

The mfcc creation configuration is the following with the TARGETKIND changed for each kind.

TARGETKIND     = MFCC_0_D_A_N_E
ZMEANSOURCE    = TRUE
TARGETRATE     = 100000.0
SAVECOMPRESSED = FALSE
SAVEWITHCRC    = FALSE
WINDOWSIZE     = 250000.0
USEHAMMING     = TRUE
PREEMCOEF      = 0.969
NUMCHANS       = 24
CEPLIFTER      = 22
NUMCEPS        = 12

The language model is created from the list of words in the sentences using 'HBuild wordList LanguageModel'. The idea is to create the weakest language model so that the acoustic model will dominate the results.

HVite is used as the decode method. HDecode is likely a better operational choice but implementation of the HVite method using the weak language model is fairly direct.

HVite -A -D -T 1 -H ./interim_files/hmm15/macros -H ./interim_files/hmm15/hmmdefs -S ./train.scp_01 -l '*' \
-i hvite/recout.mlf_01 -w /voxforge/master/LMNet -p 0.0 -s 5.0 ./interim_files/dict2 ./interim_files/tiedlist

'No tokens at beam 250' and 'No tokens at final node' give the count of those messages obtained from the HVite run just after the message 'realign hmm7' seen in the above VoxForge acoustic model generation bash file. These counts provide a measure of acoustic model quality from that HVite run.

Click a column heading to sort on that column.

The company I currently work for has a strategic goal to use speech recognition. However, this work is done on my own without their coordination, time or equipment.

The Acoustic Model Obtained Table is sorted in Word % Correct descending order. This may be the preferred ranking of the models in that the HTK Book on page 79, the line after the 5.9 section heading, holds that the Differential Coefficient models appearing higher in this ranking are better models.

Word Accuracy is sometimes remarked as a good indicator and you can sort on that column by clicking its heading. A second click will put the values in descending order. This result shows a significant shift in the model rankings with the models having more parameters, including the Differential Coefficient models, tending toward the bottom. The opposite is the case for the initial Word % Correct order. The model MFCC_0_D_A_T_Z gives the best example of this by ranking four, toward the top, in Word % Correct order and last in Word Accuracy order.

All of the models having the N parameter failed during the initial HCopy, mfcc assembly, step. It may be that I have not used the parameter properly or it may not work in the current HTK version.

The model parameter combinations having the three parameters 0, D, and E failed and yet the models with just 0 and E, and D and E worked.

P.S. If anyone has any information on who the author is, please let me know so I can contact him/her and make sure it is OK post this info here. 

--- (Edited on 3/20/2011 9:28 pm [GMT-0400] by kmaclean) ---