“ASR Status Report 2016-11-21”版本间的差异

• fighting with kazak speech recognition system:because the huge size of HCLG.fst the decoding job always make the sever done.

There are several method I have tried

• change the size or word list and corpus this method not worked very well
• prune the LM .And the parameter been used to prune the LM is 2e-7 the size of LM reduce from 290M to 60M but the result about wer is very poor
• I have upload some result about several experiment to CVSS[1]

• there are too much private affairs about myself so the job about visualization last week has been delayed I will try my best to finish it the week

• Learn MFCC extraction mechanism
• Read kaldi computer-feature code and find how to change MFCC
• Replay test

• Joint-training on SRE and LRE (LRE task). [2]
  • Tdnn is better than LSTM.
  • LRE is a long-term task.
• Briefly overview Interspeech SRE-related papers.
• CSLT-Replay detection.
  • Baseline done.
  • performance-driven based Freq-Weighting and Freq-Warping --> Yixiang.

• LRE task.
• Replay detection.

• read papers about highway connection and multi-task

• Explore the language recognition model on speech+language joint training, find how to use languange information.
• finish ML-book

• kazaka recognition baseline finished here

• figuie of ml-book read paper nn visualization

• Motify the ML-book and read paper.
• Prepare the replay detection baseline.[3]

• Complete the replay baseline and attempt to modify MFCC calculation.

• Complete the Joint-training on TASLP (speaker parts). [4]
• Joint-training on SRE and LRE (Still over-fitting !). [5]
• Read some papers and download four database. [6]
• CSLT-Replay detection database is OK! [/work4/lilt/Replay]

• Joint-training on SRE and LRE.
• Baseline system on replay detection.

• finished the additinal experiments of joint learning (speech & spk) for taslp (multi-task, ivector as part of input)[7].
• prepare a brief review of interspeech16.

• report for Weekly Reading (a brief review of interspeech16);
• joint training for bilingual: language scores as decoding mask, explore the best info receivier by studying single tasks with extra info.