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| − | !Date!!People !! Last Week !! This Week | + | ! Date!!People !! Last Week !! This Week |
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| | | rowspan="5"|2016.11.21 | | | rowspan="5"|2016.11.21 |
| | |Hang Luo | | |Hang Luo |
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| − | * | + | * Explore the language recognition models including: |
| | + | * Evaluate the model in the aspect of sentence and frame, find the accuracy is very high. |
| | + | * Minimize the language model, train it single and joint with speech model, evaluate its result. |
| | || | | || |
| − | * | + | * Continue doing the basic explore of joint training. |
| − | * | + | * Read paper about multi-language recognition models and others. |
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| | * training with shared layers; | | * training with shared layers; |
| − | * | + | * explore single tasks. |
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| | !Date!!People !! Last Week !! This Week | | !Date!!People !! Last Week !! This Week |
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| | | rowspan="5"|2016.11.14 | | | rowspan="5"|2016.11.14 |
| Date |
People |
Last Week |
This Week
|
| 2016.11.21
|
Hang Luo
|
- Explore the language recognition models including:
- Evaluate the model in the aspect of sentence and frame, find the accuracy is very high.
- Minimize the language model, train it single and joint with speech model, evaluate its result.
|
- Continue doing the basic explore of joint training.
- Read paper about multi-language recognition models and others.
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| Ying Shi
|
- 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]
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- 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
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| Yixiang Chen
|
- Learn MFCC extraction mechanism.
- Read kaldi computer-feature code and find how to change MFCC.
- Frequency-weighting based feature extraction.
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- Continue replay detection (Freq-Weighting and Freq-Warping).
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| Lantian Li
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- 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 (Freq / Mel domain).
- performance-driven based Freq-Weighting and Freq-Warping --> Yixiang.
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- LRE task.
- Replay detection.
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| Zhiyuan Tang
|
- report for Weekly Reading (a brief review of interspeech16), just prepared;
- language scores as decoding mask (1.multiply probability, very bad; 2.add log-softmax, a little bad)
- training with mask failed
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- training with shared layers;
- explore single tasks.
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| Date |
People |
Last Week |
This Week
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| 2016.11.14
|
Hang Luo
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- read papers about highway connection and multi-task
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- Explore the language recognition model on speech+language joint training, find how to use languange information.
- finish ML-book
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| Ying Shi
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- kazaka recognition baseline finished here
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- figuie of ml-book read paper nn visualization
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| Yixiang Chen
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- Motify the ML-book and read paper.
- Prepare the replay detection baseline.[3]
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- Complete the replay baseline and attempt to modify MFCC calculation.
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| Lantian Li
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- 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]
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- Joint-training on SRE and LRE.
- Baseline system on replay detection.
|
| Zhiyuan Tang
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- 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.
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- 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.
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