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4.5.1 N-Best Generation

Until this point, we have only been determining the best hypothesis for an utterance. What if we need to determine the ten or twenty best hypotheses? Of course the software makes many other "guesses", but we usually only need to know the best guess. For this experiment, we will generate the five best hypotheses and store the results in a transcription database.

Go to the directory

$ISIP_TUTORIAL/sections/s04/s04_05_p01/

Run the command:

isip_recognize -parameter_file params_decode.sof -list $ISIP_TUTORIA./databases/lists/identifiers_test.sof -verbose ALL

This will produce the following output:

Command: isip_recognize -parameter_file params_decode.sof -list $ISIP_TUTORIA./databases/lists/identifiers_test.sof -verbose ALL
Version: 1.16 (not released) 2002/09/25 00:20:53
  
loading front-end: ../../recipes/frontend.sof
loading language model: ../../models/lm_model_update.sof
loading acoustic model: ../../models/ac_model_update.sof
loading audio database: ./audio_db.sof
opening the output file: ./hypo.out
  
processing file 1 (st_9z59362a): ../../features/st_9z59362a.sof
    
ref:    
hyp[1]:    NINE ZERO FIVE NINE THREE SIX TWO 
score[1]:  -18749.466796875   frames: 294

ref:
hyp[2]:    NINE ZERO FOUR NINE THREE SIX TWO 
score[2]:  -18750.454832132   frames: 294

ref:
hyp[3]:    NINE ZERO FIVE NINE THREE SIX SIX 
score[3]:  -18751.543258765   frames: 294

ref:
hyp[4]:    FIVE ZERO FIVE NINE THREE SIX TWO 
score[4]:  -18753.238435132   frames: 294

ref:
hyp[5]:    FIVE ZERO FIVE THREE THREE SIX TWO 
score[5]:  -18755.321462354   frames: 294

processed 1 file(s) successfully, attempted 1 file(s), 294 frame(s)

In this experiment, the output file contains N best hypotheses instead of just one best hypothesis. The score values indicate the likelihood of each of the N best hypotheses. The lower the likelihood, the lower the confidence is about a hypothesis. Note that the best hypothesis has the best overall likelihood. Section 4.5: N-Best example

As discussed above, the output generated by N-Best begins with the best hypothesis followed by other possibilities in decreasing likelihood. This output format consisting of a list of hypotheses is commomly known as a N-best list. N-best lists are popular because they can be postprocessed by many natural language processing tools, and can be reordered based on their grammatical and semantic content. The N-best lists are also popular in the hybrid HMM-SVM based systems. In such systems, the first recognition pass is performed using the conventional continuous density HMM based system to generate the N-best lists. These N-best lists are then used to perform a second recognition pass using an SVM based system.

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