From this thread: For Noisy Input

For a recognition result like this:

### read waveform input
Stat: adin_file: input speechfile: seven.wav
STAT: 12447 samples (1.56 sec.)
STAT: ### speech analysis (waveform -> MFCC)
### Recognition: 1st pass (LR beam)
............................................................................pass1_best: <s> 5
pass1_best_wordseq: 0 2
pass1_best_phonemeseq: sil | f ay v
pass1_best_score: -1867.966309
### Recognition: 2nd pass (RL heuristic best-first)
STAT: 00 _default: 120 generated, 120 pushed, 14 nodes popped in 76
sentence1: <s> 5 </s>
wseq1: 0 2 1
phseq1: sil | f ay v | sil
cmscore1: 1.000 0.316 1.000
score1: -1944.799561

(tpavelka's post): Julius outputs two types of scores:

The Viterbi score, e.g.:

score1: -1944.799561

This is the cummulative score of the most likeli HMM path. The Viterbi algorithm (decoder) is just a graph search which compares scores of all possible paths through the HMM and outputs the best one. The problem is, that a score of a path (sentence) depends on the sound files length but also on the sound file itself (see this thread for more discussion). This means that Viterbi scores for different files are not comparable. I understand that you want some kind of measure, which can tell you something about whether the result found by Julius is believable or not. In that case, have a look at

The confidence score, in your example:

cmscore1: 1.000 0.316 1.000

Julius outputs a separate score for each word, so in your example the starting silence has confidence score of 1.0 (i.e. 100%), the word "five" has the score 0.316 (i.e. not that reliable) and the ending silence has again 1.0.