[blackwing]

Hello Halle, thank you for your advice, it works.
I rise vadThreshold to 3.6, it can recognize my commands now. But there are still some questions.

1. the logs said:

The word 小宝向右转 was not found in the dictionary of the acoustic model /var/mobile/Applications/830BE127-C119-44E0-B2AA-48CB37746BD3/OpenEarsSampleApp.app/AcousticModelChinese.bundle. Now using the fallback method to look it up.

Q: Dose it matters? If I add this command to the LanguageModelGeneratorLookupList.text, will it help to speed up the recognition process ?

2. about the vad:

-vad_postspeech 50 69
-vad_prespeech 20 10
-vad_startspeech 10 10
-vad_threshold 2.0 3.600000e+00

Q: How can I set the vad_postspeech and vad_prespeech params ?

3.
my recogniztion log:

2016-05-10 12:31:44.898 OpenEarsSampleApp[779:1903] Speech detected…
2016-05-10 12:31:44.900 OpenEarsSampleApp[779:60b] Local callback: Pocketsphinx has detected speech.
2016-05-10 12:31:51.251 OpenEarsSampleApp[779:3807] End of speech detected…
INFO: cmn_prior.c(131): cmn_prior_update: from < 11.49 0.22 -0.25 -0.06 -0.42 -0.11 -0.17 -0.21 -0.23 -0.07 -0.12 -0.16 -0.13 >
INFO: cmn_prior.c(149): cmn_prior_update: to < 11.70 0.08 -0.15 -0.05 -0.42 -0.11 -0.19 -0.22 -0.21 -0.10 -0.13 -0.14 -0.14 >
INFO: ngram_search_fwdtree.c(1553): 886 words recognized (4/fr)
INFO: ngram_search_fwdtree.c(1555): 15516 senones evaluated (64/fr)
INFO: ngram_search_fwdtree.c(1559): 3930 channels searched (16/fr), 158 1st, 2778 last
INFO: ngram_search_fwdtree.c(1562): 1285 words for which last channels evaluated (5/fr)
INFO: ngram_search_fwdtree.c(1564): 24 candidate words for entering last phone (0/fr)
INFO: ngram_search_fwdtree.c(1567): fwdtree 7.78 CPU 3.203 xRT
INFO: ngram_search_fwdtree.c(1570): fwdtree 14.73 wall 6.062 xRT
INFO: ngram_search_fwdflat.c(302): Utterance vocabulary contains 6 words
2016-05-10 12:31:51.253 OpenEarsSampleApp[779:60b] Local callback: Pocketsphinx has detected a second of silence, concluding an utterance.
INFO: ngram_search_fwdflat.c(948): 793 words recognized (3/fr)
INFO: ngram_search_fwdflat.c(950): 13900 senones evaluated (57/fr)
INFO: ngram_search_fwdflat.c(952): 5411 channels searched (22/fr)
INFO: ngram_search_fwdflat.c(954): 1759 words searched (7/fr)
INFO: ngram_search_fwdflat.c(957): 276 word transitions (1/fr)
INFO: ngram_search_fwdflat.c(960): fwdflat 2.00 CPU 0.825 xRT
INFO: ngram_search_fwdflat.c(963): fwdflat 1.96 wall 0.808 xRT
INFO: ngram_search.c(1280): lattice start node <s>.0 end node </s>.183
INFO: ngram_search.c(1306): Eliminated 2 nodes before end node
INFO: ngram_search.c(1411): Lattice has 241 nodes, 507 links
INFO: ps_lattice.c(1380): Bestpath score: -3737
INFO: ps_lattice.c(1384): Normalizer P(O) = alpha(</s>:183:241) = -214253
INFO: ps_lattice.c(1441): Joint P(O,S) = -235609 P(S|O) = -21356
INFO: ngram_search.c(899): bestpath 0.00 CPU 0.000 xRT
INFO: ngram_search.c(902): bestpath 0.00 wall 0.001 xRT
2016-05-10 12:31:53.221 OpenEarsSampleApp[779:3807] Pocketsphinx heard “小宝去充电” with a score of (-21356) and an utterance ID of 14.
2016-05-10 12:31:53.224 OpenEarsSampleApp[779:60b] Flite sending interrupt speech request.
2016-05-10 12:31:53.225 OpenEarsSampleApp[779:60b] Local callback: The received hypothesis is [ 小宝去充电 ] with a score of -21356 and an ID of 14

the interval is from 2016-05-10 12:31:44.898 to 2016-05-10 12:31:53.225

It took about 10s to finsh one recognition, how can I reduce the time?

4. Cpu usage is still high

When it detects speech, the cpu usage rises to about 100% and the peak lasts for about 6 seconds.

Q: Is there any method to lower the cpu usage ?

[blackwing]

Hello Halle,
1. the A5 device is iPad mini, the cpu usage peak seams last for ever, the usage stay steadily between 98% to 101%.

2. My vocabulary is small, only eight commands. It’s as follow :
NSArray *firstLanguageArray = @[@”小宝前进”,
@”小宝后退”,
@”小宝向左转”,
@”小宝向右转”,
@”小宝抬头”,
@”小宝低头”,
@”小宝去充电”,
@”小宝停止充电”];

3. It’s quite quiet around and I try it on A8X cpu device, it’s fast with the same condition.

[blackwing]

Thank you for you professional reply.

[blackwing]

Thank you for you quick reply.
In the dir Framework\OpenEars.framework, the file “OpenEars” is nearly 70MB, if I only need offline speech recognize for some commands, how can I reduce the weight further?

[Author]

Posts