“ASR Status Report 2017-12-25”版本间的差异

• some function for voice-printer

**here

• CTC for Haibo Wang
• QRcode
• ivector baseline for kazak-uyghur LRE performance is 81.85% (Utt level)

• Finish voice-checker copyright and submit the copyright in this Wednesday

• 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.

• 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

• 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

• 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

• easy-to-read interfaces for Parrot

• phone-level likelihood for detail diagnosis and an alpha version Parrot for test inside lab