Sphinx Guideline version 1

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For FSG, make changes in line no. 3 and line no. 11 (-mode fsg). In case of any error while decoding, check the file LogOutfiles/1500spkr_s1000_g16.log.txt. There are some additional parameters for decoding which one can be varied to improve accuracy such as: Parameters Default values beam 1.00E-055 pbeam 1.00E-050 wbeam 1.00E-035 subvqbeam 3.00E-003 maxwpf 20 maxhistpf 100 maxhmmpf 2000 ci_pbeam 1.00E-080 ds 1.00E-080 lw 9.5 wip 0.7 Nlextree 3 Most of the parameters above are related to pruning in the Viterbi algorithm. To understand decoding and pruning in sphinx in detail, go through the following presentation which explains the Viterbi decoding for single word as well as continuous speech recognition, Pruning, Lextree structure and consideration of Language weight while decoding. Presentation The link below is a document which shows some of the experiments conducted by my colleague by varying the parameters and observing the accuracy. Tuning parameters 3.4.1 Output of the decoder 1. $database_out.txt 2. $database_hypseg.txt $database_out.txt will look like this: kRusxii utpanna baajaara samitii (F13MH01A0011I302_silRemoved) pikaacaa baajaara samitii (F13MH01A0011I303_silRemoved) donashe baaviisa trepanna caarashe ekasasxtxa (F13MH01A0011I304_silRemoved) gahuu (F13MH01A0011I305_silRemoved) jvaarii (F13MH01A0011I306_silRemoved) makaa (F13MH01A0011I307_silRemoved) E.g. makaa is the output of file F13MH01A0011I307_silRemoved.wav 25
$database_hypseg.txt will look like this: F13MH01A0011I302_silRemoved S -1381719 T -2663753 A -2646045 L -17708 0 168656 808 6 -928199 -16508 kRusxii 37 -868399 -548 utpanna 77 -133383 -252 baajaara 111 -956937 - 192 samitii 159 72217 -1016 165 The acoustic likelihoods for each word as seen in the decoder are scaled at each frame by the maximum score for that frame. The final (total) scaling factor is written out in the decoder HYPSEG output as the number following the letter "S". T= Total score without the scaling factor. (The real score is the sum of S and T) A = Total Acoustic Score L = Total Language Model Score T = A + L. Every word or is preceded by 3 numbers. E.g. the word kRusxii is preceded by 3 numbers 6, - 928199, -16508. The first no. is the start frame, second no. is the Acoustic score and the third no. is the Language model score for that word. 26
Page 1 and 2: GUIDELINES FOR USING SPHINX VERSION
Page 3 and 4: 3.6 Word Lattice and n-best list ge
Page 5 and 6: 2.1 Overview of training Chapter 2
Page 7 and 8: Note that the words , and are tre
Page 9 and 10: The scripts at above location are a
Page 11 and 12: 3. If the speech data is sampled at
Page 13 and 14: ${database}_s1000_g16.cd_cont_1000_
Page 15 and 16: 2.6.3 FLAT INITIALIZATION OF CI MOD
Page 17 and 18: • contains all the triphones whic
Page 19 and 20: 2.7.2 Force Aligned training Someti
Page 21 and 22: 3.1 Theory Chapter 3 Sphinx Decodin
Page 23 and 24: sphinx3_lm_convert -i data.lm -o da
Page 25 and 26: After you have created the JSGF gra
Page 27: Follow the example below: In this c
Page 31 and 32: We run the sphinx3_decode by giving
Page 33 and 34: FrameIndex 0 0 Phone Phone SIL SIL
Page 35 and 36: Where: T = Total score A = Acoustic
Page 37 and 38: To get the same format as of the ou

Sphinx Guideline version 1

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