“Sinovoice-2014-12-10”版本间的差异
来自cslt Wiki
| 第66行： | 第66行： | ||
:* std::exp/std::log result in very slow computation in train203. Solved the problem by replacing to standard exp() and log(). | :* std::exp/std::log result in very slow computation in train203. Solved the problem by replacing to standard exp() and log(). | ||
:* The RT of the latest decoder on train203 is 0.25 | :* The RT of the latest decoder on train203 is 0.25 | ||
| + | |||
| + | * Online decoder | ||
| + | :* Chao will focus on interface change and CMN adaptation. | ||
2014年2月11日 (二) 14:00的最后版本
目录
DNN training
Environment setting
- Another 3 3T disks are ready for RAID-0.
- Another GPU machine was purchased. Add 4 3T disks to construct RAID-0.
Corpora
- Scripts for confidence generation is ready for auto transcription.
- 300h telephone speech data (Sinovoice recording) were done.
- Adaptation data 900 sentences ready.
470 hour 8k training
- 300h incremental training (IT) done
| Model | CE | MPE1 | MPE2 | MPE3 | MPE4 |
|---|---|---|---|---|---|
| 4k states | 23.27/22.85 | 21.35/18.87 | 21.18/18.76 | 21.07/18.54 | 20.93/18.32 |
| 8k states | 22.16/22.22 | 20.55/18.03 | 20.36/17.94 | 20.32/17.78 | 20.29/17.80 |
| 8k states + IT | - | 20.04/17.38 | 20.01/17.32 | 20.07/17.44 | 19.94/17.65 |
6000 hour 16k training
- Ran CE DNN to iteration 5 (8400 states, 80000 pdf)
- Testing results go down to 13.77% WER (Sinovoice results: 11.78).
| Model | WER | RT |
|---|---|---|
| small LM, it 4, -5/-9 | 15.80 | 1.18 |
| large LM, it 4, -5/-9 | 15.30 | 1.50 |
| large LM, it 4, -6/-9 | 15.36 | 1.30 |
| large LM, it 4, -7/-9 | 15.25 | 1.30 |
| large LM, it 5, -5/-9 | 14.17 | 1.10 |
| large LM, it 5, -5/-10 | 13.77 | 1.29 |
Adaptation
- Code ready for direct adaptation, insertion adaptation and KL-regularized adaptatoin
- 50 sentences for adaptation, 834 sentences for testing
- WER from 14.56 to 11.13
- Hidden layer adaptation is better than input and output adaptation
- Before Linear adaptation is better than after-linear adaptation
- Results are here
DNN Decoder
- Comparison between CLG and HCLG decoder
- CLG decoder uses less memory in decoding
- HCLG is faster and more accurate than CLG, and more amiable to beam control here
- Faster decoder
- std::exp/std::log result in very slow computation in train203. Solved the problem by replacing to standard exp() and log().
- The RT of the latest decoder on train203 is 0.25
- Online decoder
- Chao will focus on interface change and CMN adaptation.
