“ASR Status Report 2017-12-25”版本间的差异

2017年12月25日 (一) 05:35的版本

Date	People	Last Week	This Week
2017.12.25
	Ying Shi	some function for voice-printer speaker vector per utterance here speaker vector minus base speaker vector here CTC for Haibo Wang (Token accuracy on train set 92.80%, on cv set 89.74%) haven't test on test set QRcode speaker vector merge phone grayscale here speaker vector merge phone black-and-white map here speaker vector merge phone black-and-white map minus base vector here ivector baseline for kazak-uyghur LRE performance is 81.85% (Utt level)	Finish voice-checker copyright and submit the copyright in this Wednesday
	Lantian Li	Complete the recipe for `VV_FACTOR`. 16K and 8K deep speaker model comparison.[1]	Patent for `VV_QuickMark`. Complete the demo for `VV_FACTOR`.[Assign to Shouyi Dai] Phonetic speaker embedding. Overlap training for speaker features.
	Zhiyuan Tang	word level pronunciation accuracy based on likelihood (tell which word is well pronounced as '0' or badly pronounced '1')	model adaptation if possible, an alpha version Parrot for test inside lab to collect some data for better configurature

Date	People	Last Week	This Week	Task Tracking
2017.12.18
	Ying Shi	Finish the Voice-printer program Apply the software copyright of Voice-printer APSIPA 2017	Finish the software copyright of Voice-checker Baseline of similar language recongnition system(i-vector, DNN, PTN)	focus on function other than UI i-vector LID first
	Lantian Li	Optimize the demo of `VV_Seg` and `VV_QuickMark`. Phone-aware scorning on deep speaker feature. [2]	Phone-aware scorning. Overlap training for speaker features.	test on trivial dataset
	Zhiyuan Tang	easy-to-read interfaces for Parrot	phone-level likelihood for detail diagnosis and an alpha version Parrot for test inside lab

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 ** speaker vector per utterance  [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/6/63/SpkerVector2.png here]
 ** speaker vector minus base speaker vector [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/6/6b/Spkear_vector.png here]
+* CTC for Haibo Wang (Token accuracy on train set 92.80%, on cv set 89.74%) haven't test on test set
+* QRcode
 ** speaker vector merge phone grayscale [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/f3/Speaker_factor_gray.png here]
 ** speaker vector merge phone black-and-white map  [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/9/97/1514176866%281%29.png here]
 ** speaker vector merge phone black-and-white map minus base vector  [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/4/4e/SpeakerQrCode2.png here]
-* CTC for Haibo Wang
-* QRcode
 * ivector baseline for kazak-uyghur LRE performance is 81.85% (Utt level)
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