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第17行: |
第17行: |
| |Hui Tang | | |Hui Tang |
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− | * | + | * finish checking the speech database |
| + | * help jiayin learn kaldi by training a language identification model |
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− | * | + | * make sure which type of voice is what we need |
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Date |
People |
Last Week |
This Week
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2017.7.17
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Miao Zhang
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- Read the paralinguistic paper and material from Teacher Li
- work out the recording plan (delayed)
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- work out the recording plan with instruction from Teacher Li
- check the book of deep learning
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Hui Tang
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- finish checking the speech database
- help jiayin learn kaldi by training a language identification model
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- make sure which type of voice is what we need
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Yanqing Wang
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- check the former conclusions in a narrow network ( not finished yet )
- read the source code as a preparation for retrain task.
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- finish checking the former conclusions and try to find the applicable conditions.
- finish the retrain task.
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Ying Shi
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Yixiang Chen
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- Through voiceprint spectrum synthetic speech
- read paralinguistic and challenge of paralinguistic 2009-2017
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Lantian Li
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Zhiyuan Tang
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- Replace the old version kaldi with new ones. (delayed)
- Gather Part 1: 'Speech, Speech Processing and Tools' of Kaldi book for further release. (delayed)
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- Replace the old version kaldi with new ones.
- Gather Part 1: 'Speech, Speech Processing and Tools' of Kaldi book for further release.
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Date |
People |
Last Week |
This Week
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2017.7.10
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Miao Zhang
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- A report about trivial events
- Finish the test website with Tanghui
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- Read the paper of Paralinguistics
- Make a plan for recording and start to record hopefully.
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Hui Tang
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- completed the test web site web
- finished checking the subset of our speech databases (nearly 800 sentences)
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- finish checking the reminder of the databases (nearly 2500 sentences)
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Yanqing Wang
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- use different activation function to prune result
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- make the network narrow and test the former conclusions.
- change source code to retrain the neural network.
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Ying Shi
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- help zheling to finish his first crawler program
- chech hazak speech data
- train: 1346 utterances' WER large than 20% (utterance level WER)
- test: 759 utterances' WER large than 20%(utterance level WER)
- transfer learning based on th30 and wsj (performance is poor)
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- tools for speech data checking
- transfer learning based on large Chinese ASR model
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Yixiang Chen
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- plot for “learning deep speaker features”
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- comprehend Paralinguistics
- record voice
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Lantian Li
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- deep speaker feature
- segmentation is still not suitable.
- visualization with the t-sne seems cool.
- help Zhangzy decode d-vector and re-train a new deep speaker model.
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- more details of segmentation experiments.
- prepare the weekly meeting.
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Zhiyuan Tang
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- Scanned the source code of auto-scoring system;
- A report about the research of the speech group (Thursday).
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- Replace the old version kaldi with new ones.
- Gather Part 1: 'Speech, Speech Processing and Tools' of Kaldi book for further release.
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